Machine learning in aerodynamic shape optimization

Li, Jichao; Du, Xiaosong; Martins, Joaquim R. R. A.

doi:10.1016/j.paerosci.2022.100849

Machine learning in aerodynamic shape optimization

Journal Article · Sat Oct 01 00:00:00 EDT 2022 · Progress in Aerospace Sciences

DOI:https://doi.org/10.1016/j.paerosci.2022.100849· OSTI ID:1889499

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Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI ID:: 1889499

Journal Information:: Progress in Aerospace Sciences, Journal Name: Progress in Aerospace Sciences Journal Issue: C Vol. 134; ISSN 0376-0421

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (319)

Efficient and robust algorithms for solution of the adjoint compressible Navier-Stokes equations with applications Dwight, Richard P.; Brezillon, Joël International Journal for Numerical Methods in Fluids, Vol. 60, Issue 4 https://doi.org/10.1002/fld.1894	journal	June 2009
CAD-based shape optimisation with CFD using a discrete adjoint Xu, Shenren; Jahn, Wolfram; Müller, Jens-Dominik International Journal for Numerical Methods in Fluids, Vol. 74, Issue 3 https://doi.org/10.1002/fld.3844	journal	September 2013
On the performance of discrete adjoint CFD codes using automatic differentiation Müller, J. -D.; Cusdin, P. International Journal for Numerical Methods in Fluids, Vol. 47, Issue 8-9 https://doi.org/10.1002/fld.885	journal	January 2005
A Mathematical Theory of Communication Shannon, C. E. Bell System Technical Journal, Vol. 27, Issue 3 https://doi.org/10.1002/j.1538-7305.1948.tb01338.x	journal	July 1948
Nonlinear model order reduction based on local reduced-order bases: NONLINEAR MODEL REDUCTION BASED ON LOCAL REDUCED-ORDER BASES Amsallem, David; Zahr, Matthew J.; Farhat, Charbel International Journal for Numerical Methods in Engineering, Vol. 92, Issue 10 https://doi.org/10.1002/nme.4371	journal	June 2012
Introduction to manifold learning: Introduction to manifold learning Izenman, Alan Julian Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 4, Issue 5 https://doi.org/10.1002/wics.1222	journal	July 2012
Surrogate-Based Aerodynamic Shape Optimization of a Wing-Body Transport Aircraft Configuration Han, Zhong-Hua; Abu-Zurayk, Mohammad; Görtz, Stefan Notes on Numerical Fluid Mechanics and Multidisciplinary Design https://doi.org/10.1007/978-3-319-72020-3_16	book	January 2018
Parametric Airfoils and Wings Sobieczky, Helmut Notes on Numerical Fluid Mechanics (NNFM) https://doi.org/10.1007/978-3-322-89952-1_4	book	January 1999
Drag Reduction of Transonic Wings with Surrogate-Based Optimization Li, Jichao; Cai, Jinsheng; Qu, Kun Lecture Notes in Electrical Engineering https://doi.org/10.1007/978-981-13-3305-7_85	book	January 2019
Self-organized formation of topologically correct feature maps Kohonen, Teuvo Biological Cybernetics, Vol. 43, Issue 1 https://doi.org/10.1007/BF00337288	journal	January 1982
Aerodynamic design via control theory Jameson, Antony Journal of Scientific Computing, Vol. 3, Issue 3 https://doi.org/10.1007/BF01061285	journal	September 1988
Model reduction by CPOD and Kriging: Application to the shape optimization of an intake port Xiao, Manyu; Breitkopf, Piotr; Filomeno Coelho, Rajan Structural and Multidisciplinary Optimization, Vol. 41, Issue 4 https://doi.org/10.1007/s00158-009-0434-9	journal	October 2009
Surrogate modeling approximation using a mixture of experts based on EM joint estimation Bettebghor, Dimitri; Bartoli, Nathalie; Grihon, Stéphane Structural and Multidisciplinary Optimization, Vol. 43, Issue 2 https://doi.org/10.1007/s00158-010-0554-2	journal	August 2010
Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes Lambe, Andrew B.; Martins, Joaquim R. R. A. Structural and Multidisciplinary Optimization, Vol. 46, Issue 2 https://doi.org/10.1007/s00158-012-0763-y	journal	January 2012
Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction Bouhlel, Mohamed Amine; Bartoli, Nathalie; Otsmane, Abdelkader Structural and Multidisciplinary Optimization, Vol. 53, Issue 5 https://doi.org/10.1007/s00158-015-1395-9	journal	January 2016
Efficient aerodynamic shape optimization of transonic wings using a parallel infilling strategy and surrogate models Liu, J.; Song, W. -P.; Han, Z. -H. Structural and Multidisciplinary Optimization, Vol. 55, Issue 3 https://doi.org/10.1007/s00158-016-1546-7	journal	August 2016
Robust aerodynamic shape design based on an adaptive stochastic optimization framework Wu, Xiaojing; Zhang, Weiwei; Song, Shufang Structural and Multidisciplinary Optimization, Vol. 57, Issue 2 https://doi.org/10.1007/s00158-017-1766-5	journal	August 2017
Surrogate-based aerodynamic shape optimization with the active subspace method Li, Jichao; Cai, Jinsheng; Qu, Kun Structural and Multidisciplinary Optimization, Vol. 59, Issue 2 https://doi.org/10.1007/s00158-018-2073-5	journal	August 2018
Constraint aggregation for large number of constraints in wing surrogate-based optimization Zhang, Ke-Shi; Han, Zhong-Hua; Gao, Zhong-Jian Structural and Multidisciplinary Optimization, Vol. 59, Issue 2 https://doi.org/10.1007/s00158-018-2074-4	journal	September 2018
OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization Gray, Justin S.; Hwang, John T.; Martins, Joaquim R. R. A. Structural and Multidisciplinary Optimization, Vol. 59, Issue 4 https://doi.org/10.1007/s00158-019-02211-z	journal	March 2019
Modeling and optimization with Gaussian processes in reduced eigenbases Gaudrie, David; Le Riche, Rodolphe; Picheny, Victor Structural and Multidisciplinary Optimization, Vol. 61, Issue 6 https://doi.org/10.1007/s00158-019-02458-6	journal	February 2020
Scalable gradient–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes Bouhlel, Mohamed Amine; He, Sicheng; Martins, Joaquim R. R. A. Structural and Multidisciplinary Optimization, Vol. 61, Issue 4 https://doi.org/10.1007/s00158-020-02488-5	journal	March 2020
Accelerating the convergence of steady adjoint equations by dynamic mode decomposition Chen, Wengang; Zhang, Weiwei; Liu, Yilang Structural and Multidisciplinary Optimization, Vol. 62, Issue 2 https://doi.org/10.1007/s00158-020-02531-5	journal	February 2020
A generalized hierarchical co-Kriging model for multi-fidelity data fusion Zhou, Qi; Wu, Yuda; Guo, Zhendong Structural and Multidisciplinary Optimization, Vol. 62, Issue 4 https://doi.org/10.1007/s00158-020-02583-7	journal	May 2020
Data-driven design exploration method using conditional variational autoencoder for airfoil design Yonekura, Kazuo; Suzuki, Katsuyuki Structural and Multidisciplinary Optimization, Vol. 64, Issue 2 https://doi.org/10.1007/s00158-021-02851-0	journal	July 2021
Optimum Aerodynamic Design Using the Navier-Stokes Equations Jameson, A.; Martinelli, L.; Pierce, N. A. Theoretical and Computational Fluid Dynamics, Vol. 10, Issue 1-4 https://doi.org/10.1007/s001620050060	journal	January 1998
Performance study of gradient-enhanced Kriging Ulaganathan, Selvakumar; Couckuyt, Ivo; Dhaene, Tom Engineering with Computers, Vol. 32, Issue 1 https://doi.org/10.1007/s00366-015-0397-y	journal	February 2015
Gradient-enhanced kriging for high-dimensional problems Bouhlel, Mohamed A.; Martins, Joaquim R. R. A. Engineering with Computers, Vol. 35, Issue 1 https://doi.org/10.1007/s00366-018-0590-x	journal	February 2018
Prediction of aerodynamic flow fields using convolutional neural networks Bhatnagar, Saakaar; Afshar, Yaser; Pan, Shaowu Computational Mechanics, Vol. 64, Issue 2 https://doi.org/10.1007/s00466-019-01740-0	journal	June 2019
Physics-informed neural networks (PINNs) for fluid mechanics: a review Cai, Shengze; Mao, Zhiping; Wang, Zhicheng Acta Mechanica Sinica, Vol. 37, Issue 12 https://doi.org/10.1007/s10409-021-01148-1	journal	December 2021
Mixture of experts: a literature survey Masoudnia, Saeed; Ebrahimpour, Reza Artificial Intelligence Review, Vol. 42, Issue 2 https://doi.org/10.1007/s10462-012-9338-y	journal	May 2012
Kriging surrogate model with coordinate transformation based on likelihood and gradient Namura, Nobuo; Shimoyama, Koji; Obayashi, Shigeru Journal of Global Optimization, Vol. 68, Issue 4 https://doi.org/10.1007/s10898-017-0516-y	journal	April 2017
Multifidelity Modeling by Polynomial Chaos-Based Cokriging to Enable Efficient Model-Based Reliability Analysis of NDT Systems Du, Xiaosong; Leifsson, Leifur Journal of Nondestructive Evaluation, Vol. 39, Issue 1 https://doi.org/10.1007/s10921-020-0656-8	journal	January 2020
Medical image analysis based on deep learning approach Puttagunta, Muralikrishna; Ravi, S. Multimedia Tools and Applications https://doi.org/10.1007/s11042-021-10707-4	journal	April 2021
Wing aerodynamic optimization using efficient mathematically-extracted modal design variables Allen, Christian B.; Poole, Daniel J.; Rendall, Thomas C. S. Optimization and Engineering, Vol. 19, Issue 2 https://doi.org/10.1007/s11081-018-9376-7	journal	March 2018
A Survey of Bayesian Calibration and Physics-informed Neural Networks in Scientific Modeling Viana, Felipe A. C.; Subramaniyan, Arun K. Archives of Computational Methods in Engineering, Vol. 28, Issue 5 https://doi.org/10.1007/s11831-021-09539-0	journal	February 2021
DLR project Digital-X: towards virtual aircraft design and flight testing based on high-fidelity methods Kroll, N.; Abu-Zurayk, M.; Dimitrov, D. CEAS Aeronautical Journal, Vol. 7, Issue 1 https://doi.org/10.1007/s13272-015-0179-7	journal	December 2015
Reduced-order models for aerodynamic applications, loads and MDO Ripepi, M.; Verveld, M. J.; Karcher, N. W. CEAS Aeronautical Journal, Vol. 9, Issue 1 https://doi.org/10.1007/s13272-018-0283-6	journal	February 2018
On the application of surrogate regression models for aerodynamic coefficient prediction Andrés-Pérez, Esther; Paulete-Periáñez, Carlos Complex & Intelligent Systems https://doi.org/10.1007/s40747-021-00307-y	journal	March 2021
An Overview of Restricted Boltzmann Machines Upadhya, Vidyadhar; Sastry, P. S. Journal of the Indian Institute of Science, Vol. 99, Issue 2 https://doi.org/10.1007/s41745-019-0102-z	journal	February 2019
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design Owoyele, Opeoluwa; Pal, Pinaki Applied Energy, Vol. 285 https://doi.org/10.1016/j.apenergy.2021.116455	journal	March 2021
Expedited constrained multi-objective aerodynamic shape optimization by means of physics-based surrogates Koziel, Slawomir; Tesfahunegn, Yonatan A.; Leifsson, Leifur Applied Mathematical Modelling, Vol. 40, Issue 15-16 https://doi.org/10.1016/j.apm.2016.03.020	journal	August 2016
Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function Han, Zhong-Hua; Görtz, Stefan; Zimmermann, Ralf Aerospace Science and Technology, Vol. 25, Issue 1 https://doi.org/10.1016/j.ast.2012.01.006	journal	March 2013
Artificial neural network based inverse design: Airfoils and wings Sun, Gang; Sun, Yanjie; Wang, Shuyue Aerospace Science and Technology, Vol. 42 https://doi.org/10.1016/j.ast.2015.01.030	journal	April 2015
Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis Liem, Rhea P.; Mader, Charles A.; Martins, Joaquim R. R. A. Aerospace Science and Technology, Vol. 43 https://doi.org/10.1016/j.ast.2015.02.019	journal	June 2015
Supercritical natural laminar flow airfoil optimization for regional aircraft wing design Zhang, Yufei; Fang, Xiaoming; Chen, Haixin Aerospace Science and Technology, Vol. 43 https://doi.org/10.1016/j.ast.2015.02.024	journal	June 2015
Supercritical wing design based on airfoil optimization and 2.75D transformation Zhao, Tong; Zhang, Yufei; Chen, Haixin Aerospace Science and Technology, Vol. 56 https://doi.org/10.1016/j.ast.2016.07.010	journal	September 2016
Expected drag minimization for aerodynamic design optimization based on aircraft operational data Liem, Rhea P.; Martins, Joaquim R. R. A.; Kenway, Gaetan K. W. Aerospace Science and Technology, Vol. 63 https://doi.org/10.1016/j.ast.2017.01.006	journal	April 2017
Wing-body junction optimisation with CAD-based parametrisation including a moving intersection Xu, Shenren; Timme, Sebastian; Mykhaskiv, Orest Aerospace Science and Technology, Vol. 68 https://doi.org/10.1016/j.ast.2017.06.014	journal	September 2017
A fast-prediction surrogate model for large datasets Hwang, John T.; Martins, Joaquim R. R. A. Aerospace Science and Technology, Vol. 75 https://doi.org/10.1016/j.ast.2017.12.030	journal	April 2018
On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization Yu, Yin; Lyu, Zhoujie; Xu, Zelu Aerospace Science and Technology, Vol. 75 https://doi.org/10.1016/j.ast.2018.01.016	journal	April 2018
Aerodynamic optimization of helicopter rear fuselage Batrakov, A. S.; Kusyumov, A. N.; Mikhailov, S. A. Aerospace Science and Technology, Vol. 77 https://doi.org/10.1016/j.ast.2018.03.046	journal	June 2018
A PCA–ANN-based inverse design model of stall lift robustness for high-lift device Wang, Xinyu; Wang, Shuyue; Tao, Jun Aerospace Science and Technology, Vol. 81 https://doi.org/10.1016/j.ast.2018.08.019	journal	October 2018
Aerodynamic inverse design using multifidelity models and manifold mapping Du, Xiaosong; Ren, Jie; Leifsson, Leifur Aerospace Science and Technology, Vol. 85 https://doi.org/10.1016/j.ast.2018.12.008	journal	February 2019
Robust aerodynamic shape optimization—From a circle to an airfoil He, Xiaolong; Li, Jichao; Mader, Charles A. Aerospace Science and Technology, Vol. 87 https://doi.org/10.1016/j.ast.2019.01.051	journal	April 2019
Aerodynamic shape optimization using a novel optimizer based on machine learning techniques Yan, Xinghui; Zhu, Jihong; Kuang, Minchi Aerospace Science and Technology, Vol. 86 https://doi.org/10.1016/j.ast.2019.02.003	journal	March 2019
Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design Bartoli, N.; Lefebvre, T.; Dubreuil, S. Aerospace Science and Technology, Vol. 90 https://doi.org/10.1016/j.ast.2019.03.041	journal	July 2019
Mode multigrid - A novel convergence acceleration method Liu, Yilang; Zhang, Weiwei; Kou, Jiaqing Aerospace Science and Technology, Vol. 92 https://doi.org/10.1016/j.ast.2019.06.001	journal	September 2019
Data-driven constraint approach to ensure low-speed performance in transonic aerodynamic shape optimization Li, Jichao; He, Sicheng; Martins, Joaquim R. R. A. Aerospace Science and Technology, Vol. 92 https://doi.org/10.1016/j.ast.2019.06.008	journal	September 2019
Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization Tao, Jun; Sun, Gang Aerospace Science and Technology, Vol. 92 https://doi.org/10.1016/j.ast.2019.07.002	journal	September 2019
Optimum aerodynamic shape design under uncertainty by utility theory and metamodeling Du, Xiaosong; Leifsson, Leifur Aerospace Science and Technology, Vol. 95 https://doi.org/10.1016/j.ast.2019.105464	journal	December 2019
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization Renganathan, S. Ashwin; Maulik, Romit; Ahuja, Jai Aerospace Science and Technology, Vol. 111 https://doi.org/10.1016/j.ast.2021.106522	journal	April 2021
Surrogate-based aerodynamic shape optimization for delaying airfoil dynamic stall using Kriging regression and infill criteria Raul, Vishal; Leifsson, Leifur Aerospace Science and Technology, Vol. 111 https://doi.org/10.1016/j.ast.2021.106555	journal	April 2021
On deep-learning-based geometric filtering in aerodynamic shape optimization Li, Jichao; Zhang, Mengqi Aerospace Science and Technology, Vol. 112 https://doi.org/10.1016/j.ast.2021.106603	journal	May 2021
Data-based approach for wing shape design optimization Li, Jichao; Zhang, Mengqi Aerospace Science and Technology, Vol. 112 https://doi.org/10.1016/j.ast.2021.106639	journal	May 2021
Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling Du, Xiaosong; He, Ping; Martins, Joaquim R. R. A. Aerospace Science and Technology, Vol. 113 https://doi.org/10.1016/j.ast.2021.106701	journal	June 2021
Deep learning based multistage method for inverse design of supercritical airfoil Lei, Ruiwu; Bai, Junqiang; Wang, Hui Aerospace Science and Technology, Vol. 119 https://doi.org/10.1016/j.ast.2021.107101	journal	December 2021
Low-Reynolds-number airfoil design optimization using deep-learning-based tailored airfoil modes Li, Jichao; Zhang, Mengqi; Tay, Chien Ming Jonathan Aerospace Science and Technology, Vol. 121 https://doi.org/10.1016/j.ast.2021.107309	journal	February 2022
Aerodynamic design optimization of nacelle/pylon position on an aircraft Li, Jing; Gao, Zhenghong; Huang, Jiangtao Chinese Journal of Aeronautics, Vol. 26, Issue 4 https://doi.org/10.1016/j.cja.2013.04.052	journal	August 2013
Stationary flow fields prediction of variable physical domain based on proper orthogonal decomposition and kriging surrogate model Qiu, Yasong; Bai, Junqiang Chinese Journal of Aeronautics, Vol. 28, Issue 1 https://doi.org/10.1016/j.cja.2014.12.017	journal	February 2015
Unsteady aerodynamic modeling at high angles of attack using support vector machines Wang, Qing; Qian, Weiqi; He, Kaifeng Chinese Journal of Aeronautics, Vol. 28, Issue 3 https://doi.org/10.1016/j.cja.2015.03.010	journal	June 2015
Multipoint optimization on fuel efficiency in conceptual design of wide-body aircraft Chai, Xiao; Yu, Xiongqing; Wang, Yu Chinese Journal of Aeronautics, Vol. 31, Issue 1 https://doi.org/10.1016/j.cja.2017.10.006	journal	January 2018
Pressure distribution guided supercritical wing optimization Li, Runze; Deng, Kaiwen; Zhang, Yufei Chinese Journal of Aeronautics, Vol. 31, Issue 9 https://doi.org/10.1016/j.cja.2018.06.021	journal	September 2018
Performance improvement of optimization solutions by POD-based data mining Duan, Yanhui; Wu, Wenhua; Zhang, Peihong Chinese Journal of Aeronautics, Vol. 32, Issue 4 https://doi.org/10.1016/j.cja.2019.01.014	journal	April 2019
Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids Han, Zhonghua; Xu, Chenzhou; Zhang, Liang Chinese Journal of Aeronautics, Vol. 33, Issue 1 https://doi.org/10.1016/j.cja.2019.05.001	journal	January 2020
Non-intrusive reduced-order model for predicting transonic flow with varying geometries Sun, Zhiwei; Wang, Chen; Zheng, Yu Chinese Journal of Aeronautics, Vol. 33, Issue 2 https://doi.org/10.1016/j.cja.2019.12.014	journal	February 2020
Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization Tao, Jun; Sun, Gang; Guo, Liqiang Chinese Journal of Aeronautics, Vol. 33, Issue 6 https://doi.org/10.1016/j.cja.2020.01.015	journal	June 2020
Adaptive-surrogate-based robust optimization of transonic natural laminar flow nacelle Yao, Yuan; Ma, Dongli; Yang, Muqing Chinese Journal of Aeronautics, Vol. 34, Issue 10 https://doi.org/10.1016/j.cja.2021.01.007	journal	October 2021
Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics Li, Runze; Zhang, Yufei; Chen, Haixin Chinese Journal of Aeronautics, Vol. 35, Issue 4 https://doi.org/10.1016/j.cja.2021.10.028	journal	April 2022
Reduced-order fluid/structure modeling of a complete aircraft configuration Lieu, T.; Farhat, C.; Lesoinne, M. Computer Methods in Applied Mechanics and Engineering, Vol. 195, Issue 41-43 https://doi.org/10.1016/j.cma.2005.08.026	journal	August 2006
Non-intrusive reduced order modelling of the Navier–Stokes equations Xiao, D.; Fang, F.; Buchan, A. G. Computer Methods in Applied Mechanics and Engineering, Vol. 293 https://doi.org/10.1016/j.cma.2015.05.015	journal	August 2015
Computational mechanics enhanced by deep learning Oishi, Atsuya; Yagawa, Genki Computer Methods in Applied Mechanics and Engineering, Vol. 327 https://doi.org/10.1016/j.cma.2017.08.040	journal	December 2017
Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints Shi, Renhe; Liu, Li; Long, Teng Computer Methods in Applied Mechanics and Engineering, Vol. 347 https://doi.org/10.1016/j.cma.2018.12.026	journal	April 2019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks Kissas, Georgios; Yang, Yibo; Hwuang, Eileen Computer Methods in Applied Mechanics and Engineering, Vol. 358 https://doi.org/10.1016/j.cma.2019.112623	journal	January 2020
Physics-informed neural networks for high-speed flows Mao, Zhiping; Jagtap, Ameya D.; Karniadakis, George Em Computer Methods in Applied Mechanics and Engineering, Vol. 360 https://doi.org/10.1016/j.cma.2019.112789	journal	March 2020
Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization Zhang, Xinshuai; Xie, Fangfang; Ji, Tingwei Computer Methods in Applied Mechanics and Engineering, Vol. 373 https://doi.org/10.1016/j.cma.2020.113485	journal	January 2021
Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches Peter, Jacques E. V.; Dwight, Richard P. Computers & Fluids, Vol. 39, Issue 3 https://doi.org/10.1016/j.compfluid.2009.09.013	journal	March 2010
Efficient shape optimization for certain and uncertain aerodynamic design Schillings, Claudia; Schmidt, Stephan; Schulz, Volker Computers & Fluids, Vol. 46, Issue 1 https://doi.org/10.1016/j.compfluid.2010.12.007	journal	July 2011
A non-intrusive acceleration technique for compressible flow solvers based on dynamic mode decomposition Andersson, N. Computers & Fluids, Vol. 133 https://doi.org/10.1016/j.compfluid.2016.04.018	journal	July 2016
An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM He, Ping; Mader, Charles A.; Martins, Joaquim R. R. A. Computers & Fluids, Vol. 168 https://doi.org/10.1016/j.compfluid.2018.04.012	journal	May 2018
A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils Wu, Haizhou; Liu, Xuejun; An, Wei Computers & Fluids, Vol. 198 https://doi.org/10.1016/j.compfluid.2019.104393	journal	February 2020
A review on deep reinforcement learning for fluid mechanics Garnier, Paul; Viquerat, Jonathan; Rabault, Jean Computers & Fluids, Vol. 225 https://doi.org/10.1016/j.compfluid.2021.104973	journal	July 2021
Aerodynamic design optimization: Challenges and perspectives Martins, Joaquim R. R. A. Computers & Fluids, Vol. 239 https://doi.org/10.1016/j.compfluid.2022.105391	journal	May 2022
3-D high-fidelity hydrostructural optimization of cavitation-free composite lifting surfaces Liao, Yingqian; Martins, Joaquim R. R. A.; Young, Yin Lu Composite Structures, Vol. 268 https://doi.org/10.1016/j.compstruct.2021.113937	journal	July 2021
An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset Dangut, Maren David; Skaf, Zakwan; Jennions, Ian K. ISA Transactions, Vol. 113 https://doi.org/10.1016/j.isatra.2020.05.001	journal	July 2021
Choice of initial guess in iterative solution of series of systems arising in fluid flow simulations Tromeur-Dervout, Damien; Vassilevski, Yuri Journal of Computational Physics, Vol. 219, Issue 1 https://doi.org/10.1016/j.jcp.2006.03.014	journal	November 2006
Reduced-order modeling of transonic flows around an airfoil submitted to small deformations Bourguet, Rémi; Braza, Marianna; Dervieux, Alain Journal of Computational Physics, Vol. 230, Issue 1 https://doi.org/10.1016/j.jcp.2010.09.019	journal	January 2011
Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation Tripathy, Rohit; Bilionis, Ilias; Gonzalez, Marcial Journal of Computational Physics, Vol. 321 https://doi.org/10.1016/j.jcp.2016.05.039	journal	September 2016
Dynamic mode extrapolation to improve the efficiency of dual time stepping method Liu, Yi; Wang, Gang; Ye, Zhengyin Journal of Computational Physics, Vol. 352 https://doi.org/10.1016/j.jcp.2017.09.043	journal	January 2018
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
A Jacobian-free approximate Newton–Krylov startup strategy for RANS simulations Yildirim, Anil; Kenway, Gaetan K. W.; Mader, Charles A. Journal of Computational Physics, Vol. 397 https://doi.org/10.1016/j.jcp.2019.06.018	journal	November 2019
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders Lee, Kookjin; Carlberg, Kevin T. Journal of Computational Physics https://doi.org/10.1016/j.jcp.2019.108973	journal	November 2019
Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks Discacciati, Niccolò; Hesthaven, Jan S.; Ray, Deep Journal of Computational Physics, Vol. 409 https://doi.org/10.1016/j.jcp.2020.109304	journal	May 2020
Gradient-based constrained optimization using a database of linear reduced-order models Choi, Youngsoo; Boncoraglio, Gabriele; Anderson, Spenser Journal of Computational Physics, Vol. 423 https://doi.org/10.1016/j.jcp.2020.109787	journal	December 2020
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data Yang, Liu; Meng, Xuhui; Karniadakis, George Em Journal of Computational Physics, Vol. 425 https://doi.org/10.1016/j.jcp.2020.109913	journal	January 2021
Direct shape optimization through deep reinforcement learning Viquerat, Jonathan; Rabault, Jean; Kuhnle, Alexander Journal of Computational Physics, Vol. 428 https://doi.org/10.1016/j.jcp.2020.110080	journal	March 2021
Customized data-driven RANS closures for bi-fidelity LES–RANS optimization Zhang, Yu; Dwight, Richard P.; Schmelzer, Martin Journal of Computational Physics, Vol. 432 https://doi.org/10.1016/j.jcp.2021.110153	journal	May 2021
Multi-condition multi-objective optimization using deep reinforcement learning Kim, Sejin; Kim, Innyoung; You, Donghyun Journal of Computational Physics, Vol. 462 https://doi.org/10.1016/j.jcp.2022.111263	journal	August 2022
High-fidelity multipoint hydrostructural optimization of a 3-D hydrofoil Garg, Nitin; Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. Journal of Fluids and Structures, Vol. 71 https://doi.org/10.1016/j.jfluidstructs.2017.02.001	journal	May 2017
Aerodynamic shape optimization of civil structures: A CFD-enabled Kriging-based approach Bernardini, Enrica; Spence, Seymour M. J.; Wei, Daniel Journal of Wind Engineering and Industrial Aerodynamics, Vol. 144 https://doi.org/10.1016/j.jweia.2015.03.011	journal	September 2015
Linear versus nonlinear dimensionality reduction for banks’ credit rating prediction Orsenigo, C.; Vercellis, C. Knowledge-Based Systems, Vol. 47 https://doi.org/10.1016/j.knosys.2013.03.001	journal	July 2013
A comprehensive survey on support vector machine classification: Applications, challenges and trends Cervantes, Jair; Garcia-Lamont, Farid; Rodríguez-Mazahua, Lisbeth Neurocomputing, Vol. 408 https://doi.org/10.1016/j.neucom.2019.10.118	journal	September 2020
Reduced-order modeling: new approaches for computational physics Lucia, David J.; Beran, Philip S.; Silva, Walter A. Progress in Aerospace Sciences, Vol. 40, Issue 1-2 https://doi.org/10.1016/j.paerosci.2003.12.001	journal	February 2004
Surrogate-based analysis and optimization Queipo, Nestor V.; Haftka, Raphael T.; Shyy, Wei Progress in Aerospace Sciences, Vol. 41, Issue 1 https://doi.org/10.1016/j.paerosci.2005.02.001	journal	January 2005
Recent advances in surrogate-based optimization Forrester, Alexander I. J.; Keane, Andy J. Progress in Aerospace Sciences, Vol. 45, Issue 1-3 https://doi.org/10.1016/j.paerosci.2008.11.001	journal	January 2009
Reduced order unsteady aerodynamic modeling for stability and control analysis using computational fluid dynamics Ghoreyshi, Mehdi; Jirásek, Adam; Cummings, Russell M. Progress in Aerospace Sciences, Vol. 71 https://doi.org/10.1016/j.paerosci.2014.09.001	journal	November 2014
A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses Yondo, Raul; Andrés, Esther; Valero, Eusebio Progress in Aerospace Sciences, Vol. 96 https://doi.org/10.1016/j.paerosci.2017.11.003	journal	January 2018
Effective adjoint approaches for computational fluid dynamics Kenway, Gaetan K. W.; Mader, Charles A.; He, Ping Progress in Aerospace Sciences, Vol. 110 https://doi.org/10.1016/j.paerosci.2019.05.002	journal	October 2019
Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion Zhang, Mengmeng; Bartoli, Nathalie; Jungo, Aidan Progress in Aerospace Sciences, Vol. 119 https://doi.org/10.1016/j.paerosci.2020.100647	journal	November 2020
Enhancing optimization capabilities using the AGILE collaborative MDO framework with application to wing and nacelle design Lefebvre, T.; Bartoli, N.; Dubreuil, S. Progress in Aerospace Sciences, Vol. 119 https://doi.org/10.1016/j.paerosci.2020.100649	journal	November 2020
Data-driven modeling for unsteady aerodynamics and aeroelasticity Kou, Jiaqing; Zhang, Weiwei Progress in Aerospace Sciences, Vol. 125 https://doi.org/10.1016/j.paerosci.2021.100725	journal	August 2021
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network Sherstinsky, Alex Physica D: Nonlinear Phenomena, Vol. 404 https://doi.org/10.1016/j.physd.2019.132306	journal	March 2020
Inverse Design of Transonic Airfoils Using Variable-Resolution Modeling and Pressure Distribution Alignment Leifsson, Leifur; Koziel, Slawomir; Ogurtsov, Stanislav Procedia Computer Science, Vol. 4 https://doi.org/10.1016/j.procs.2011.04.133	journal	January 2011
Global sensitivity analysis using polynomial chaos expansions Sudret, Bruno Reliability Engineering & System Safety, Vol. 93, Issue 7 https://doi.org/10.1016/j.ress.2007.04.002	journal	July 2008
Polynomial chaos expansion for sensitivity analysis Crestaux, Thierry; Le Maıˆtre, Olivier; Martinez, Jean-Marc Reliability Engineering & System Safety, Vol. 94, Issue 7 https://doi.org/10.1016/j.ress.2008.10.008	journal	July 2009
A review of novelty detection Pimentel, Marco A. F.; Clifton, David A.; Clifton, Lei Signal Processing, Vol. 99 https://doi.org/10.1016/j.sigpro.2013.12.026	journal	June 2014
Uncertainty quantification and robust design optimization applied to aircraft propulsion systems Panzeri, Marco; Savelyev, Andrey; Anisimov, Kirill Transportation Research Procedia, Vol. 29 https://doi.org/10.1016/j.trpro.2018.02.026	journal	January 2018
A gentle introduction to deep learning in medical image processing Maier, Andreas; Syben, Christopher; Lasser, Tobias Zeitschrift für Medizinische Physik, Vol. 29, Issue 2 https://doi.org/10.1016/j.zemedi.2018.12.003	journal	May 2019
Dynamic mode decomposition of numerical and experimental data Schmid, Peter J. Journal of Fluid Mechanics, Vol. 656 https://doi.org/10.1017/S0022112010001217	journal	July 2010
Solving inverse problems using data-driven models Arridge, Simon; Maass, Peter; Öktem, Ozan Acta Numerica, Vol. 28 https://doi.org/10.1017/S0962492919000059	journal	May 2019
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy Journal of Fluid Mechanics, Vol. 807 https://doi.org/10.1017/jfm.2016.615	journal	October 2016
Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control Rabault, Jean; Kuchta, Miroslav; Jensen, Atle Journal of Fluid Mechanics, Vol. 865 https://doi.org/10.1017/jfm.2019.62	journal	February 2019
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks Cai, Shengze; Wang, Zhicheng; Fuest, Frederik Journal of Fluid Mechanics, Vol. 915 https://doi.org/10.1017/jfm.2021.135	journal	March 2021
Efficient Global Optimization of Expensive Black-Box Functions Jones, Donald R.; Schonlau, Matthias; Welch, William J. Journal of Global Optimization, Vol. 13, Issue 4, p. 455-492 https://doi.org/10.1023/A:1008306431147	journal	January 1998
Human-level control through deep reinforcement learning Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David Nature, Vol. 518, Issue 7540 https://doi.org/10.1038/nature14236	journal	February 2015
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science Mocanu, Decebal Constantin; Mocanu, Elena; Stone, Peter Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-04316-3	journal	June 2018
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-48114-3	journal	August 2019
Physics-informed machine learning Karniadakis, George Em; Kevrekidis, Ioannis G.; Lu, Lu Nature Reviews Physics, Vol. 3, Issue 6 https://doi.org/10.1038/s42254-021-00314-5	journal	May 2021
A probabilistic graphical model foundation for enabling predictive digital twins at scale Kapteyn, Michael G.; Pretorius, Jacob V. R.; Willcox, Karen E. Nature Computational Science, Vol. 1, Issue 5 https://doi.org/10.1038/s43588-021-00069-0	journal	May 2021
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder Jin, Xiaowei; Cheng, Peng; Chen, Wen-Li Physics of Fluids, Vol. 30, Issue 4 https://doi.org/10.1063/1.5024595	journal	April 2018
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil Renganathan, S. Ashwin; Maulik, Romit; Rao, Vishwas Physics of Fluids, Vol. 32, Issue 4 https://doi.org/10.1063/1.5144661	journal	April 2020
Deep neural networks for nonlinear model order reduction of unsteady flows Eivazi, Hamidreza; Veisi, Hadi; Naderi, Mohammad Hossein Physics of Fluids, Vol. 32, Issue 10 https://doi.org/10.1063/5.0020526	journal	October 2020
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries Kashefi, Ali; Rempe, Davis; Guibas, Leonidas J. Physics of Fluids, Vol. 33, Issue 2 https://doi.org/10.1063/5.0033376	journal	February 2021
Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework Wang, Jing; He, Cheng; Li, Runze Physics of Fluids, Vol. 33, Issue 8 https://doi.org/10.1063/5.0053979	journal	August 2021
Learning data-driven discretizations for partial differential equations Bar-Sinai, Yohai; Hoyer, Stephan; Hickey, Jason Proceedings of the National Academy of Sciences, Vol. 116, Issue 31 https://doi.org/10.1073/pnas.1814058116	journal	July 2019
Machine learning–accelerated computational fluid dynamics Kochkov, Dmitrii; Smith, Jamie A.; Alieva, Ayya Proceedings of the National Academy of Sciences, Vol. 118, Issue 21 https://doi.org/10.1073/pnas.2101784118	journal	May 2021
Sequential Design and Analysis of High-Accuracy and Low-Accuracy Computer Codes Xiong, Shifeng; Qian, Peter Z. G.; Wu, C. F. Jeff Technometrics, Vol. 55, Issue 1 https://doi.org/10.1080/00401706.2012.723572	journal	August 2012
Kriging: a method of interpolation for geographical information systems Oliver, M. A.; Webster, R. International journal of geographical information systems, Vol. 4, Issue 3 https://doi.org/10.1080/02693799008941549	journal	July 1990
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization Sasena, Michael J.; Papalambros, Panos; Goovaerts, Pierre Engineering Optimization, Vol. 34, Issue 3 https://doi.org/10.1080/03052150211751	journal	January 2002
Infill sampling criteria for surrogate-based optimization with constraint handling Parr, J. M.; Keane, A. J.; Forrester, A. I. J. Engineering Optimization, Vol. 44, Issue 10 https://doi.org/10.1080/0305215X.2011.637556	journal	October 2012
Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method Amine Bouhlel, Mohamed; Bartoli, Nathalie; Regis, Rommel G. Engineering Optimization, Vol. 50, Issue 12 https://doi.org/10.1080/0305215X.2017.1419344	journal	February 2018
Interpolation-based reduced-order modelling for steady transonic flows via manifold learning Franz, T.; Zimmermann, R.; Görtz, S. International Journal of Computational Fluid Dynamics, Vol. 28, Issue 3-4 https://doi.org/10.1080/10618562.2014.918695	journal	March 2014
Random forests: from early developments to recent advancements Fawagreh, Khaled; Gaber, Mohamed Medhat; Elyan, Eyad Systems Science & Control Engineering, Vol. 2, Issue 1 https://doi.org/10.1080/21642583.2014.956265	journal	January 2014
Research on aerodynamic shape optimization of trains with different dimensional design variables Zhang, Le; Li, Tian; Zhang, Jiye International Journal of Rail Transportation, Vol. 9, Issue 5 https://doi.org/10.1080/23248378.2020.1817803	journal	September 2020
Airfoil aerodynamics optimization under uncertain operating conditions No authors listed Journal of Physics: Conference Series, Vol. 1446, Issue 1 https://doi.org/10.1088/1742-6596/1446/1/012014	journal	January 2020
Market segmentation for profit maximization using machine learning algorithms Janardhanan, Sruthi; Muthalagu, Raja Journal of Physics: Conference Series, Vol. 1706, Issue 1 https://doi.org/10.1088/1742-6596/1706/1/012160	journal	December 2020
Aerodynamic Design of Megawatt Wind Turbine Blades with NPU-WA Airfoils Xu, Jianhua; Han, Zhonghua; Yan, Xiaochao IOP Conference Series: Earth and Environmental Science, Vol. 495, Issue 1 https://doi.org/10.1088/1755-1315/495/1/012018	journal	April 2020
Turbulence and the dynamics of coherent structures. II. Symmetries and transformations Sirovich, Lawrence Quarterly of Applied Mathematics, Vol. 45, Issue 3 https://doi.org/10.1090/qam/910463	journal	January 1987
Multi-fidelity optimization via surrogate modelling Forrester, Alexander I. J.; Sóbester, András; Keane, Andy J. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 463, Issue 2088 https://doi.org/10.1098/rspa.2007.1900	journal	September 2007
Principal component analysis: a review and recent developments Jolliffe, Ian T.; Cadima, Jorge Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, Issue 2065 https://doi.org/10.1098/rsta.2015.0202	journal	April 2016
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng Physical Review Fluids, Vol. 2, Issue 3 https://doi.org/10.1103/PhysRevFluids.2.034603	journal	March 2017
Learned discretizations for passive scalar advection in a two-dimensional turbulent flow Zhuang, Jiawei; Kochkov, Dmitrii; Bar-Sinai, Yohai Physical Review Fluids, Vol. 6, Issue 6 https://doi.org/10.1103/PhysRevFluids.6.064605	journal	June 2021
Adaptive sampling strategies for non‐intrusive POD‐based surrogates Guénot, Marc; Lepot, Ingrid; Sainvitu, Caroline Engineering Computations, Vol. 30, Issue 4 https://doi.org/10.1108/02644401311329352	journal	May 2013
Artificial neural networks to predict aerodynamic coefficients of transport airplanes Secco, Ney Rafael; Mattos, Bento Silva de Aircraft Engineering and Aerospace Technology, Vol. 89, Issue 2 https://doi.org/10.1108/AEAT-05-2014-0069	journal	March 2017
Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization Koziel, Slawomir; Tesfahunegn, Yonatan; Leifsson, Leifur Engineering Computations, Vol. 33, Issue 8 https://doi.org/10.1108/EC-09-2015-0277	journal	November 2016
A survey of decision tree classifier methodology Safavian, S. R.; Landgrebe, D. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, Issue 3 https://doi.org/10.1109/21.97458	journal	January 1991
Support vector machines Hearst, M. A.; Dumais, S. T.; Osuna, E. IEEE Intelligent Systems and their Applications, Vol. 13, Issue 4 https://doi.org/10.1109/5254.708428	journal	July 1998
Learning long-term dependencies with gradient descent is difficult Bengio, Y.; Simard, P.; Frasconi, P. IEEE Transactions on Neural Networks, Vol. 5, Issue 2 https://doi.org/10.1109/72.279181	journal	March 1994
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture Min, Erxue; Guo, Xifeng; Liu, Qiang IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2855437	journal	January 2018
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges Usama, Muhammad; Qadir, Junaid; Raza, Aunn IEEE Access, Vol. 7 https://doi.org/10.1109/ACCESS.2019.2916648	journal	January 2019
Analysis of Dimensionality Reduction Techniques on Big Data Reddy, G. Thippa; Reddy, M. Praveen Kumar; Lakshmanna, Kuruva IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2980942	journal	January 2020
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN Li, Shuai; Li, Wanqing; Cook, Chris 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2018.00572	conference	June 2018
Taking the Human Out of the Loop: A Review of Bayesian Optimization Shahriari, Bobak; Swersky, Kevin; Wang, Ziyu Proceedings of the IEEE, Vol. 104, Issue 1 https://doi.org/10.1109/JPROC.2015.2494218	journal	January 2016
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems von Rueden, Laura; Mayer, Sebastian; Beckh, Katharina IEEE Transactions on Knowledge and Data Engineering https://doi.org/10.1109/TKDE.2021.3079836	journal	January 2021
A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures Li, Shaopeng; Snaiki, Reda; Wu, Teng Computer-Aided Civil and Infrastructure Engineering, Vol. 36, Issue 6 https://doi.org/10.1111/mice.12655	journal	March 2021
Robust Design Optimization Under Mixed Uncertainties With Stochastic Expansions Zhang, Yi; Hosder, Serhat Journal of Mechanical Design, Vol. 135, Issue 8 https://doi.org/10.1115/1.4024230	journal	June 2013
Conservative Surrogate Model Using Weighted Kriging Variance for Sampling-Based RBDO Zhao, Liang; Choi, K. K.; Lee, Ikjin Journal of Mechanical Design, Vol. 135, Issue 9 https://doi.org/10.1115/1.4024731	journal	July 2013
A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis Hu, Zhen; Mahadevan, Sankaran Journal of Mechanical Design, Vol. 138, Issue 6 https://doi.org/10.1115/1.4033428	journal	May 2016
Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing Xiong, Yi; Duong, Pham Luu Trung; Wang, Dong Journal of Mechanical Design, Vol. 141, Issue 10 https://doi.org/10.1115/1.4043587	journal	May 2019
A Machine Learning Enabled Multi-Fidelity Platform for the Integrated Design of Aircraft Systems Garriga, Ana Garcia; Mainini, Laura; Ponnusamy, Sangeeth Saagar Journal of Mechanical Design, Vol. 141, Issue 12 https://doi.org/10.1115/1.4044401	journal	October 2019
Generalizability of Convolutional Encoder–Decoder Networks for Aerodynamic Flow-Field Prediction Across Geometric and Physical-Fluidic Variations Tangsali, Kaustubh; Krishnamurthy, Vinayak R.; Hasnain, Zohaib Journal of Mechanical Design, Vol. 143, Issue 5 https://doi.org/10.1115/1.4048221	journal	November 2020
Design, analysis and test of a novel cylinder-driven mode applied to microgripper Chen, Xiaodong; Xie, Zm; Tan, Huifeng Journal of Mechanical Design https://doi.org/10.1115/1.4053043	journal	November 2021
A Global Geometric Framework for Nonlinear Dimensionality Reduction Tenenbaum, J. B. Science, Vol. 290, Issue 5500 https://doi.org/10.1126/science.290.5500.2319	journal	December 2000
Nonlinear Dimensionality Reduction by Locally Linear Embedding Roweis, S. T. Science, Vol. 290, Issue 5500 https://doi.org/10.1126/science.290.5500.2323	journal	December 2000
Lagrange Duality Theory for Convex Control Problems Hager, William W.; Mitter, Sanjoy K. SIAM Journal on Control and Optimization, Vol. 14, Issue 5 https://doi.org/10.1137/0314054	journal	August 1976
Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces Constantine, Paul G.; Dow, Eric; Wang, Qiqi SIAM Journal on Scientific Computing, Vol. 36, Issue 4 https://doi.org/10.1137/130916138	journal	January 2014
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems Benner, Peter; Gugercin, Serkan; Willcox, Karen SIAM Review, Vol. 57, Issue 4 https://doi.org/10.1137/130932715	journal	January 2015
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization Peherstorfer, Benjamin; Willcox, Karen; Gunzburger, Max SIAM Review, Vol. 60, Issue 3 https://doi.org/10.1137/16M1082469	journal	January 2018
The Shifted Proper Orthogonal Decomposition: A Mode Decomposition for Multiple Transport Phenomena Reiss, J.; Schulze, P.; Sesterhenn, J. SIAM Journal on Scientific Computing, Vol. 40, Issue 3 https://doi.org/10.1137/17M1140571	journal	January 2018
SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization Gill, Philip E.; Murray, Walter; Saunders, Michael A. SIAM Review, Vol. 47, Issue 1 https://doi.org/10.1137/S0036144504446096	journal	January 2005
Direct manipulation of free-form deformations Hsu, William M.; Hughes, John F.; Kaufman, Henry ACM SIGGRAPH Computer Graphics, Vol. 26, Issue 2 https://doi.org/10.1145/142920.134036	journal	July 1992
Free-form deformation of solid geometric models Sederberg, Thomas W.; Parry, Scott R. ACM SIGGRAPH Computer Graphics, Vol. 20, Issue 4 https://doi.org/10.1145/15886.15903	journal	August 1986
ImageNet classification with deep convolutional neural networks Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey E. Communications of the ACM, Vol. 60, Issue 6 https://doi.org/10.1145/3065386	journal	May 2017
Deep Learning for Anomaly Detection: A Review Pang, Guansong; Shen, Chunhua; Cao, Longbing ACM Computing Surveys, Vol. 54, Issue 2 https://doi.org/10.1145/3439950	journal	March 2022
Deep Learning in Medical Image Analysis Shen, Dinggang; Wu, Guorong; Suk, Heung-Il Annual Review of Biomedical Engineering, Vol. 19, Issue 1 https://doi.org/10.1146/annurev-bioeng-071516-044442	journal	June 2017
Statistical Mechanics of Deep Learning Bahri, Yasaman; Kadmon, Jonathan; Pennington, Jeffrey Annual Review of Condensed Matter Physics, Vol. 11, Issue 1 https://doi.org/10.1146/annurev-conmatphys-031119-050745	journal	March 2020
Turbulence Modeling in the Age of Data Duraisamy, Karthik; Iaccarino, Gianluca; Xiao, Heng Annual Review of Fluid Mechanics, Vol. 51, Issue 1 https://doi.org/10.1146/annurev-fluid-010518-040547	journal	January 2019
Machine Learning for Fluid Mechanics Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros Annual Review of Fluid Mechanics, Vol. 52, Issue 1 https://doi.org/10.1146/annurev-fluid-010719-060214	journal	September 2019
Transport Aircraft Conceptual Design Optimization Using Real Coded Genetic Algorithm Singh, Vedant; Sharma, Somesh K.; Vaibhav, S. International Journal of Aerospace Engineering, Vol. 2016 https://doi.org/10.1155/2016/2813541	journal	January 2016
An Improved Approach for Estimating the Hyperparameters of the Kriging Model for High-Dimensional Problems through the Partial Least Squares Method Bouhlel, Mohamed Amine; Bartoli, Nathalie; Otsmane, Abdelkader Mathematical Problems in Engineering, Vol. 2016 https://doi.org/10.1155/2016/6723410	journal	January 2016
Deep Learning for Computer Vision: A Brief Review Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios Computational Intelligence and Neuroscience, Vol. 2018 https://doi.org/10.1155/2018/7068349	journal	January 2018
Addressing Overfitting Problem in Deep Learning-Based Solutions for Next Generation Data-Driven Networks Xiao, Mansheng; Wu, Yuezhong; Zuo, Guocai Wireless Communications and Mobile Computing, Vol. 2021 https://doi.org/10.1155/2021/8493795	journal	August 2021
Training Products of Experts by Minimizing Contrastive Divergence Hinton, Geoffrey E. Neural Computation, Vol. 14, Issue 8 https://doi.org/10.1162/089976602760128018	journal	August 2002
GTM: The Generative Topographic Mapping Bishop, Christopher M.; Svensén, Markus; Williams, Christopher K. I. Neural Computation, Vol. 10, Issue 1 https://doi.org/10.1162/089976698300017953	journal	January 1998
Long Short-Term Memory Hochreiter, Sepp; Schmidhuber, Jürgen Neural Computation, Vol. 9, Issue 8 https://doi.org/10.1162/neco.1997.9.8.1735	journal	November 1997
A Fast Learning Algorithm for Deep Belief Nets Hinton, Geoffrey E.; Osindero, Simon; Teh, Yee-Whye Neural Computation, Vol. 18, Issue 7 https://doi.org/10.1162/neco.2006.18.7.1527	journal	July 2006
Support Vector Machines Guenther, Nick; Schonlau, Matthias The Stata Journal: Promoting communications on statistics and Stata, Vol. 16, Issue 4 https://doi.org/10.1177/1536867X1601600407	journal	December 2016
An industrial view on numerical simulation for aircraft aerodynamic design Abbas-Bayoumi, Adel; Becker, Klaus Journal of Mathematics in Industry, Vol. 1, Issue 1 https://doi.org/10.1186/2190-5983-1-10	journal	January 2011
A survey on Image Data Augmentation for Deep Learning Shorten, Connor; Khoshgoftaar, Taghi M. Journal of Big Data, Vol. 6, Issue 1 https://doi.org/10.1186/s40537-019-0197-0	journal	July 2019
A comprehensive survey of anomaly detection techniques for high dimensional big data Thudumu, Srikanth; Branch, Philip; Jin, Jiong Journal of Big Data, Vol. 7, Issue 1 https://doi.org/10.1186/s40537-020-00320-x	journal	July 2020
Clustering algorithms: A comparative approach Rodriguez, Mayra Z.; Comin, Cesar H.; Casanova, Dalcimar PLOS ONE, Vol. 14, Issue 1 https://doi.org/10.1371/journal.pone.0210236	journal	January 2019
A Survey on Prevention of Overfitting in Convolution Neural Networks Using Machine Learning Techniques M. R. Narasinga Rao, Dr; Venkatesh Prasad, V.; Sai Teja, P. International Journal of Engineering & Technology, Vol. 7, Issue 2.32 https://doi.org/10.14419/ijet.v7i2.32.15399	journal	May 2018
Aerodynamic Parameters Estimation Using Radial Basis Function Neural Partial Differentiation Method Sanwale, Jitu; Singh, Dhan Jeet Defence Science Journal, Vol. 68, Issue 3 https://doi.org/10.14429/dsj.68.11843	journal	April 2018
Generalized Low Rank Models Udell, Madeleine; Horn, Corinne; Zadeh, Reza Foundations and Trends® in Machine Learning, Vol. 9, Issue 1 https://doi.org/10.1561/2200000055	journal	January 2016
An Introduction to Deep Reinforcement Learning François-Lavet, Vincent; Henderson, Peter; Islam, Riashat Foundations and Trends® in Machine Learning, Vol. 11, Issue 3-4 https://doi.org/10.1561/2200000071	journal	January 2018
Reinforcement Learning: A Survey Kaelbling, L. P.; Littman, M. L.; Moore, A. W. Journal of Artificial Intelligence Research, Vol. 4 https://doi.org/10.1613/jair.301	journal	January 1996
An overview of the supervised machine learning methods Nasteski, Vladimir HORIZONS.B, Vol. 4 https://doi.org/10.20544/HORIZONS.B.04.1.17.P05	journal	December 2017
pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems Wu, Neil; Kenway, Gaetan; Mader, Charles Journal of Open Source Software, Vol. 5, Issue 54 https://doi.org/10.21105/joss.02564	journal	October 2020
Measurement of Inequality of Incomes Gini, Corrado The Economic Journal, Vol. 31, Issue 121 https://doi.org/10.2307/2223319	journal	March 1921
The Homogeneous Chaos Wiener, Norbert American Journal of Mathematics, Vol. 60, Issue 4 https://doi.org/10.2307/2371268	journal	October 1938
Aerodynamic Optimization of Near-future High-wing Aircraft Hashimoto, Akihiro; Jeong, Shinkyu; Obayashi, Shigeru TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, Vol. 58, Issue 2 https://doi.org/10.2322/tjsass.58.73	journal	January 2015
Multipoint and Multi-Objective Aerodynamic Shape Optimization Nemec, Marian; Zingg, David W.; Pulliam, Thomas H. AIAA Journal, Vol. 42, Issue 6 https://doi.org/10.2514/1.10415	journal	June 2004
Supervised Learning Approach to Parametric Computer-Aided Design Geometry Repair Sóbester, András; Keane, Andy J. AIAA Journal, Vol. 44, Issue 2 https://doi.org/10.2514/1.17193	journal	February 2006
Universal Parametric Geometry Representation Method Kulfan, Brenda M. Journal of Aircraft, Vol. 45, Issue 1 https://doi.org/10.2514/1.29958	journal	January 2008
Surrogate-Based Aerodynamic Shape Optimization of a Civil Aircraft Engine Nacelle Song, Wenbin; Keane, Andy J. AIAA Journal, Vol. 45, Issue 10 https://doi.org/10.2514/1.30015	journal	October 2007
Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging Laurenceau, J.; Sagaut, P. AIAA Journal, Vol. 46, Issue 2 https://doi.org/10.2514/1.32308	journal	February 2008
Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity Amsallem, David; Farhat, Charbel AIAA Journal, Vol. 46, Issue 7 https://doi.org/10.2514/1.35374	journal	July 2008
Geometric Filtration Using Proper Orthogonal Decomposition for Aerodynamic Design Optimization Toal, David J. J.; Bressloff, Neil W.; Keane, Andy J. AIAA Journal, Vol. 48, Issue 5 https://doi.org/10.2514/1.41420	journal	May 2010
Efficient Optimization Design Method Using Kriging Model Jeong, Shinkyu; Murayama, Mitsuhiro; Yamamoto, Kazuomi Journal of Aircraft, Vol. 42, Issue 2 https://doi.org/10.2514/1.6386	journal	March 2005
Airfoil Optimization Using Practical Aerodynamic Design Requirements Buckley, Howard P.; Zhou, Beckett Y.; Zingg, David W. Journal of Aircraft, Vol. 47, Issue 5 https://doi.org/10.2514/1.C000256	journal	September 2010
Efficient Multipoint Aerodynamic Design Optimization Via Cokriging Toal, David J. J.; Keane, Andy J. Journal of Aircraft, Vol. 48, Issue 5 https://doi.org/10.2514/1.C031342	journal	September 2011
Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. Journal of Aircraft, Vol. 51, Issue 1 https://doi.org/10.2514/1.C032150	journal	January 2014
Aerodynamic Shape Optimization of Common Research Model Wing–Body–Tail Configuration Chen, Song; Lyu, Zhoujie; Kenway, Gaetan K. W. Journal of Aircraft, Vol. 53, Issue 1 https://doi.org/10.2514/1.C033328	journal	January 2016
Efficient Reduced-Radial Basis Function-Based Mesh Deformation Within an Adjoint-Based Aerodynamic Optimization Framework Poirier, Vincent; Nadarajah, Siva Journal of Aircraft, Vol. 53, Issue 6 https://doi.org/10.2514/1.C033573	journal	November 2016
Wing Aerostructural Optimization Under Uncertain Aircraft Range and Payload Weight Bahamonde Jacome, Luis; Elham, Ali Journal of Aircraft, Vol. 54, Issue 3 https://doi.org/10.2514/1.C034050	journal	May 2017
Decision Tree– and Random Forest–Based Novel Unsteady Aerodynamics Modeling Using Flight Data Kumar, Ajit; Ghosh, Ajoy Kanti Journal of Aircraft, Vol. 56, Issue 1 https://doi.org/10.2514/1.C035034	journal	January 2019
High-Fidelity Design-Allocation Optimization of a Commercial Aircraft Maximizing Airline Profit Hwang, John T.; Jasa, John P.; Martins, Joaquim R. R. A. Journal of Aircraft, Vol. 56, Issue 3 https://doi.org/10.2514/1.C035082	journal	May 2019
Single- and Multipoint Aerodynamic Shape Optimization Using Multifidelity Models and Manifold Mapping Nagawkar, Jethro; Ren, Jie; Du, Xiaosong Journal of Aircraft, Vol. 58, Issue 3 https://doi.org/10.2514/1.C035297	journal	May 2021
Coupled Aeropropulsive Optimization of a Three-Dimensional Boundary-Layer Ingestion Propulsor Considering Inlet Distortion Gray, Justin S.; Mader, Charles A.; Kenway, Gaetan K. W. Journal of Aircraft, Vol. 57, Issue 6 https://doi.org/10.2514/1.C035845	journal	November 2020
Reduced-Order Model for Viscous Aerodynamic Flow Past an Airfoil Alonso, D.; Vega, J. M.; Velazquez, A. AIAA Journal, Vol. 48, Issue 9 https://doi.org/10.2514/1.J050153	journal	September 2010
Dimension Reduction for Aerodynamic Design Optimization Viswanath, Asha; Forrester, A. I. J.; Keane, A. J. AIAA Journal, Vol. 49, Issue 6 https://doi.org/10.2514/1.J050717	journal	June 2011
Physics- and Engineering Knowledge-Based Repair of Computer-Aided Design Parametric Geometries Li, Dong; Sóbester, András; Keane, Andy J. AIAA Journal, Vol. 50, Issue 6 https://doi.org/10.2514/1.J050761	journal	June 2012
Aerodynamic Shape Optimization of Wings Using a Parallel Newton-Krylov Approach Leung, Timothy M.; Zingg, David W. AIAA Journal, Vol. 50, Issue 3 https://doi.org/10.2514/1.J051192	journal	March 2012
Alternative Cokriging Method for Variable-Fidelity Surrogate Modeling Han, Zhong-Hua; Görtz, Stefan AIAA Journal, Vol. 50, Issue 5 https://doi.org/10.2514/1.J051243	journal	May 2012
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling Han, Zhong-Hua; Görtz, Stefan AIAA Journal, Vol. 50, Issue 9 https://doi.org/10.2514/1.J051354	journal	September 2012
Cokriging for Robust Design Optimization Keane, Andy J. AIAA Journal, Vol. 50, Issue 11 https://doi.org/10.2514/1.J051391	journal	November 2012
Multimodality and Global Optimization in Aerodynamic Design Chernukhin, Oleg; Zingg, David W. AIAA Journal, Vol. 51, Issue 6 https://doi.org/10.2514/1.J051835	journal	June 2013
Multidisciplinary Design Optimization: A Survey of Architectures Martins, Joaquim R. R. A.; Lambe, Andrew B. AIAA Journal, Vol. 51, Issue 9 https://doi.org/10.2514/1.J051895	journal	September 2013
Approach to Aerodynamic Design Through Numerical Optimization Buckley, Howard P.; Zingg, David W. AIAA Journal, Vol. 51, Issue 8 https://doi.org/10.2514/1.J052268	journal	August 2013
Reduced-Order Aerodynamic Modeling of Flapping Wing Energy Harvesting at Low Reynolds Number Bryant, Matthew; Gomez, Juan Carlos; Garcia, Ephrahim AIAA Journal, Vol. 51, Issue 12 https://doi.org/10.2514/1.J052364	journal	December 2013
Special Section on Multidisciplinary Design Optimization: Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? Viana, Felipe A. C.; Simpson, Timothy W.; Balabanov, Vladimir AIAA Journal, Vol. 52, Issue 4 https://doi.org/10.2514/1.J052375	journal	April 2014
Constrained Design Optimization Using Generative Topographic Mapping Viswanath, Asha; Forrester, A. I. J.; Keane, A. J. AIAA Journal, Vol. 52, Issue 5 https://doi.org/10.2514/1.J052414	journal	May 2014
Multimission Aircraft Fuel-Burn Minimization via Multipoint Aerostructural Optimization Liem, Rhea P.; Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 53, Issue 1 https://doi.org/10.2514/1.J052940	journal	January 2015
Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark Lyu, Zhoujie; Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 53, Issue 4 https://doi.org/10.2514/1.J053318	journal	April 2015
Dimensionality Reduction Using Principal Component Analysis Applied to the Gradient Berguin, Steven H.; Mavris, Dimitri N. AIAA Journal, Vol. 53, Issue 4 https://doi.org/10.2514/1.J053372	journal	April 2015
Metric-Based Mathematical Derivation of Efficient Airfoil Design Variables Poole, Daniel J.; Allen, Christian B.; Rendall, Thomas C. S. AIAA Journal, Vol. 53, Issue 5 https://doi.org/10.2514/1.J053427	journal	May 2015
Drag Minimization Based on the Navier–Stokes Equations Using a Newton–Krylov Approach Osusky, Lana; Buckley, Howard; Reist, Thomas AIAA Journal, Vol. 53, Issue 6 https://doi.org/10.2514/1.J053457	journal	June 2015
Study Based on the AIAA Aerodynamic Design Optimization Discussion Group Test Cases LeDoux, Stephen T.; Vassberg, John C.; Young, David P. AIAA Journal, Vol. 53, Issue 7 https://doi.org/10.2514/1.J053535	journal	July 2015
Evaluation of Aerodynamic Loads via Reduced-Order Methodology Fossati, Marco AIAA Journal, Vol. 53, Issue 8 https://doi.org/10.2514/1.J053755	journal	August 2015
SU2: An Open-Source Suite for Multiphysics Simulation and Design Economon, Thomas D.; Palacios, Francisco; Copeland, Sean R. AIAA Journal, Vol. 54, Issue 3 https://doi.org/10.2514/1.J053813	journal	March 2016
Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates Leifsson, Leifur; Koziel, Slawomir; Tesfahunegn, Yonatan A. AIAA Journal, Vol. 54, Issue 2 https://doi.org/10.2514/1.J054128	journal	February 2016
Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 54, Issue 1 https://doi.org/10.2514/1.J054154	journal	January 2016
Sequential Radial Basis Function Using Support Vector Machine for Expensive Design Optimization Shi, Renhe; Liu, Li; Long, Teng AIAA Journal, Vol. 55, Issue 1 https://doi.org/10.2514/1.J054832	journal	January 2017
Aerodynamic Shape Optimization for Natural Laminar Flow Using a Discrete-Adjoint Approach Rashad, Ramy; Zingg, David W. AIAA Journal, Vol. 54, Issue 11 https://doi.org/10.2514/1.J054940	journal	November 2016
Geometric Comparison of Aerofoil Shape Parameterization Methods Masters, D. A.; Taylor, N. J.; Rendall, T. C. S. AIAA Journal, Vol. 55, Issue 5 https://doi.org/10.2514/1.J054943	journal	May 2017
Buffet-Onset Constraint Formulation for Aerodynamic Shape Optimization Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 55, Issue 6 https://doi.org/10.2514/1.J055172	journal	June 2017
Adjoint-Based Two-Step Optimization Method Using Proper Orthogonal Decomposition and Domain Decomposition Li, Jichao; Cai, Jinsheng; Qu, Kun AIAA Journal, Vol. 56, Issue 3 https://doi.org/10.2514/1.J055773	journal	March 2018
Weighted Gradient-Enhanced Kriging for High-Dimensional Surrogate Modeling and Design Optimization Han, Zhong-Hua; Zhang, Yu; Song, Chen-Xing AIAA Journal, Vol. 55, Issue 12 https://doi.org/10.2514/1.J055842	journal	December 2017
Active Subspaces of Airfoil Shape Parameterizations Grey, Zachary J.; Constantine, Paul G. AIAA Journal, Vol. 56, Issue 5 https://doi.org/10.2514/1.J056054	journal	May 2018
Surrogate Modeling of Aerodynamic Simulations for Multiple Operating Conditions Using Machine Learning Dupuis, Romain; Jouhaud, Jean-Christophe; Sagaut, Pierre AIAA Journal, Vol. 56, Issue 9 https://doi.org/10.2514/1.J056405	journal	September 2018
Benchmark Aerostructural Models for the Study of Transonic Aircraft Wings Brooks, Timothy R.; Kenway, Gaetan K. W.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 56, Issue 7 https://doi.org/10.2514/1.J056603	journal	July 2018
Aerodynamic Shape Optimization of Natural-Laminar-Flow Wing Using Surrogate-Based Approach Han, Zhong-Hua; Chen, Jing; Zhang, Ke-Shi AIAA Journal, Vol. 56, Issue 7 https://doi.org/10.2514/1.J056661	journal	July 2018
Flowfield Reconstruction Method Using Artificial Neural Network Yu, Jian; Hesthaven, Jan S. AIAA Journal, Vol. 57, Issue 2 https://doi.org/10.2514/1.J057108	journal	February 2019
Data-Based Approach for Fast Airfoil Analysis and Optimization Li, Jichao; Bouhlel, Mohamed Amine; Martins, Joaquim R. R. A. AIAA Journal, Vol. 57, Issue 2 https://doi.org/10.2514/1.J057129	journal	February 2019
Multimodality in Aerodynamic Wing Design Optimization Bons, Nicolas P.; He, Xiaolong; Mader, Charles A. AIAA Journal, Vol. 57, Issue 3 https://doi.org/10.2514/1.J057294	journal	March 2019
Filter-Based Sequential Radial Basis Function Method for Spacecraft Multidisciplinary Design Optimization Shi, Renhe; Liu, Li; Long, Teng AIAA Journal, Vol. 57, Issue 3 https://doi.org/10.2514/1.J057403	journal	March 2019
Inverse Design of Airfoil Using a Deep Convolutional Neural Network Sekar, Vinothkumar; Zhang, Mengqi; Shu, Chang AIAA Journal, Vol. 57, Issue 3 https://doi.org/10.2514/1.J057894	journal	March 2019
Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows Thuerey, Nils; Weißenow, Konstantin; Prantl, Lukas AIAA Journal, Vol. 58, Issue 1 https://doi.org/10.2514/1.J058291	journal	January 2020
Flexible Formulation of Spatial Integration Constraints in Aerodynamic Shape Optimization Brelje, Benjamin J.; Anibal, Joshua L.; Yildirim, Anil AIAA Journal, Vol. 58, Issue 6 https://doi.org/10.2514/1.J058366	journal	June 2020
Multifidelity Data Fusion: Application to Blended-Wing-Body Multidisciplinary Analysis Under Uncertainty Feldstein, Alex; Lazzara, David; Princen, Norman AIAA Journal, Vol. 58, Issue 2 https://doi.org/10.2514/1.J058388	journal	February 2020
Massively Multipoint Aerodynamic Shape Design via Surrogate-Assisted Gradient-Based Optimization Li, Jichao; Cai, Jinsheng AIAA Journal, Vol. 58, Issue 5 https://doi.org/10.2514/1.J058491	journal	May 2020
DAFoam: An Open-Source Adjoint Framework for Multidisciplinary Design Optimization with OpenFOAM He, Ping; Mader, Charles A.; Martins, Joaquim R. R. A. AIAA Journal, Vol. 58, Issue 3 https://doi.org/10.2514/1.J058853	journal	March 2020
Natural Laminar-Flow Airfoil Optimization Design Using a Discrete Adjoint Approach Shi, Yayun; Mader, Charles A.; He, Sicheng AIAA Journal, Vol. 58, Issue 11 https://doi.org/10.2514/1.J058944	journal	November 2020
Gradient-Limiting Shape Control for Efficient Aerodynamic Optimization Kedward, L. J.; Allen, C. B.; Rendall, T. C. S. AIAA Journal, Vol. 58, Issue 9 https://doi.org/10.2514/1.J058977	journal	September 2020
Cross Validation of Aerodynamic Shape Optimization Methodologies for Aircraft Wing-Body Optimization Reist, Thomas A.; Koo, David; Zingg, David W. AIAA Journal, Vol. 58, Issue 6 https://doi.org/10.2514/1.J059091	journal	June 2020
Efficient Aerodynamic Shape Optimization with Deep-Learning-Based Geometric Filtering Li, Jichao; Zhang, Mengqi; Martins, Joaquim R. R. A. AIAA Journal, Vol. 58, Issue 10 https://doi.org/10.2514/1.J059254	journal	October 2020
Efficient Mesh Generation and Deformation for Aerodynamic Shape Optimization Secco, Ney R.; Kenway, Gaetan K. W.; He, Ping AIAA Journal, Vol. 59, Issue 4 https://doi.org/10.2514/1.J059491	journal	April 2021
Adjoint-Free Aerodynamic Shape Optimization of the Common Research Model Wing Li, Jichao; Zhang, Mengqi AIAA Journal, Vol. 59, Issue 6 https://doi.org/10.2514/1.J059921	journal	June 2021
Learning the Aerodynamic Design of Supercritical Airfoils Through Deep Reinforcement Learning Li, Runze; Zhang, Yufei; Chen, Haixin AIAA Journal, Vol. 59, Issue 10 https://doi.org/10.2514/1.J060189	journal	October 2021
Linear Stability-Based Smooth Reynolds-Averaged Navier–Stokes Transition Model for Aerodynamic Flows Halila, Gustavo Luiz Olichevis; Yildirim, Anil; Mader, Charles A. AIAA Journal, Vol. 60, Issue 2 https://doi.org/10.2514/1.J060481	journal	February 2022
Response Surface Techniques for Diffuser Shape Optimization Madsen, Jens I.; Shyy, Wei; Haftka, Raphael T. AIAA Journal, Vol. 38, Issue 9 https://doi.org/10.2514/2.1160	journal	September 2000
Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization Simpson, Timothy W.; Mauery, Timothy M.; Korte, John J. AIAA Journal, Vol. 39, Issue 12 https://doi.org/10.2514/2.1234	journal	December 2001
Balanced Model Reduction via the Proper Orthogonal Decomposition Willcox, K.; Peraire, J. AIAA Journal, Vol. 40, Issue 11 https://doi.org/10.2514/2.1570	journal	November 2002
Probabilistic Approach to Free-Form Airfoil Shape Optimization Under Uncertainty Huyse, Luc; Padula, Sharon L.; Lewis, R. Michael AIAA Journal, Vol. 40, Issue 9 https://doi.org/10.2514/2.1881	journal	September 2002
Response Surface Method for Airfoil Design in Transonic Flow Ahn, Jaekwon; Kim, Hyoung-Jin; Lee, Dong-Ho Journal of Aircraft, Vol. 38, Issue 2 https://doi.org/10.2514/2.2780	journal	March 2001
Concise Orthogonal Representation of Supercritical Airfoils Robinson, G. M.; Keane, A. J. Journal of Aircraft, Vol. 38, Issue 3 https://doi.org/10.2514/2.2803	journal	May 2001
Three-Dimensional Transonic Aeroelasticity Using Proper Orthogonal Decomposition-Based Reduced-Order Models Thomas, Jeffrey P.; Dowell, Earl H.; Hall, Kenneth C. Journal of Aircraft, Vol. 40, Issue 3 https://doi.org/10.2514/2.3128	journal	May 2003
Wing Design by Numerical Optimization Hicks, Raymond M.; Henne, Preston A. Journal of Aircraft, Vol. 15, Issue 7 https://doi.org/10.2514/3.58379	journal	July 1978
Formulations for Surrogate-Based Optimization with Data Fit, Multifidelity, and Reduced-Order Models Eldred, Michael; Dunlavy, Daniel 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference https://doi.org/10.2514/6.2006-7117	conference	June 2012
Effect of Shape Parameterization on Aerodynamic Shape Optimization Castonguay, Patrice; Nadarajah, Siva 45th AIAA Aerospace Sciences Meeting and Exhibit https://doi.org/10.2514/6.2007-59	conference	January 2007
A CAD-Free Approach to High-Fidelity Aerostructural Optimization Kenway, Gaetan; Kennedy, Graeme; Martins, Joaquim R. R. A. 13th AIAA/ISSMO Multidisciplinary Analysis Optimization Conference https://doi.org/10.2514/6.2010-9231	conference	June 2010
GeoMACH: Geometry-Centric MDAO of Aircraft Configurations with High Fidelity Hwang, John; Martins, Joaquim R. R. A. 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference https://doi.org/10.2514/6.2012-5605	conference	September 2012
Aerostructural Design Optimization of an Adaptive Morphing Trailing Edge Wing Burdette, David A.; Kenway, Gaetan K.; Lyu, Zhoujie 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference https://doi.org/10.2514/6.2015-1129	conference	January 2015
An adaptive optimization strategy based on mixture of experts for wing aerodynamic design optimization Bartoli, Nathalie; Lefebvre, Thierry; Dubreuil, Sylvain 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference https://doi.org/10.2514/6.2017-4433	conference	June 2017
High-Fidelity Aerodynamic Shape Optimization of a Full Configuration Regional Jet Bons, Nicolas; Mader, Charles A.; Martins, Joaquim R. R. A. 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference https://doi.org/10.2514/6.2018-0106	conference	January 2018
A data-based approach for fast airfoil analysis and optimization Li, Jichao; Bouhlel, Mohamed Amine; Martins, Joaquim R. R. A. 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference https://doi.org/10.2514/6.2018-1383	conference	January 2018
Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks Chen, Wei; Chiu, Kevin; Fuge, Mark AIAA Scitech 2019 Forum https://doi.org/10.2514/6.2019-2351	conference	January 2019
Aeropropulsive Design Optimization of a Boundary Layer Ingestion System Yildirim, Anil; Gray, Justin S.; Mader, Charles A. AIAA Aviation 2019 Forum https://doi.org/10.2514/6.2019-3455	conference	June 2019
An Introductory Review of Deep Learning for Prediction Models With Big Data Emmert-Streib, Frank; Yang, Zhen; Feng, Han Frontiers in Artificial Intelligence, Vol. 3 https://doi.org/10.3389/frai.2020.00004	journal	February 2020
Aerostructural Design Exploration of a Wing in Transonic Flow Bons, Nicolas P.; Martins, Joaquim R. R. A. Aerospace, Vol. 7, Issue 8 https://doi.org/10.3390/aerospace7080118	journal	August 2020
Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning Qin, Sheng; Wang, Shuyue; Wang, Liyue Applied Sciences, Vol. 11, Issue 1 https://doi.org/10.3390/app11010106	journal	December 2020
A K Nearest Neighborhood-Based Wind Estimation for Rotary-Wing VTOL UAVs Wang, Liyang; Misra, Gaurav; Bai, Xiaoli Drones, Vol. 3, Issue 2 https://doi.org/10.3390/drones3020031	journal	March 2019
An Inverse Design Method for Airfoils Based on Pressure Gradient Distribution Zhang, Yufei; Yan, Chongyang; Chen, Haixin Energies, Vol. 13, Issue 13 https://doi.org/10.3390/en13133400	journal	July 2020
Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV Azabi, Yousef; Savvaris, Al; Kipouros, Timoleon Machine Learning and Knowledge Extraction, Vol. 1, Issue 2 https://doi.org/10.3390/make1020033	journal	April 2019
Extrapolation-Based Acceleration of Iterative Solvers: Application to Simulation of 3D Flows Grinberg, Leopold; Karniadakis, George Em Communications in Computational Physics, Vol. 9, Issue 3 https://doi.org/10.4208/cicp.301109.080410s	journal	March 2011
Deep belief networks Hinton, Geoffrey Scholarpedia, Vol. 4, Issue 5 https://doi.org/10.4249/scholarpedia.5947	journal	January 2009
Aerodynamic Shape Optimization of an SUV in early Development Stage using a Response Surface Method Sun, Shaoyun; Chang, Yin-ping; Fu, Qiang SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 7, Issue 4 https://doi.org/10.4271/2014-01-2445	journal	September 2014
Customer Segmentation Using Clustering and Data Mining Techniques Kashwan, Kishana R.; Velu, C. M. International Journal of Computer Theory and Engineering https://doi.org/10.7763/IJCTE.2013.V5.811	journal	January 2013

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