“Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains”

Brown, Nathan K.; Garland, Anthony P.; Fadel, Georges M.; Li, Gang

doi:10.1016/j.matdes.2022.110672

Title: “Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains”

Journal Article · 31 May 2022 · Materials & Design

DOI: https://doi.org/10.1016/j.matdes.2022.110672 · OSTI ID:1867970

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; Li, Gang

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1867970

Report Number(s):: SAND2022-6621J; S0264127522002933; 110672; PII: S0264127522002933

Journal Information:: Materials & Design, Journal Name: Materials & Design Journal Issue: C Vol. 218; ISSN 0264-1275

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (40)

Modeling 3D Shapes by Reinforcement Learning Lin, Cheng; Fan, Tingxiang; Wang, Wenping Computer Vision – ECCV 2020 https://doi.org/10.1007/978-3-030-58607-2_32	book	January 2020
Optimal shape design as a material distribution problem Bendsøe, M. P. Structural Optimization, Vol. 1, Issue 4 https://doi.org/10.1007/BF01650949	journal	December 1989
Variable chromosome length genetic algorithm for progressive refinement in topology optimization Kim, I. Y.; de Weck, O. L. Structural and Multidisciplinary Optimization, Vol. 29, Issue 6 https://doi.org/10.1007/s00158-004-0498-5	journal	January 2005
Stress-based topology optimization for continua Le, Chau; Norato, Julian; Bruns, Tyler Structural and Multidisciplinary Optimization, Vol. 41, Issue 4 https://doi.org/10.1007/s00158-009-0440-y	journal	October 2009
Topology optimization approaches: A comparative review Sigmund, Ole; Maute, Kurt Structural and Multidisciplinary Optimization, Vol. 48, Issue 6 https://doi.org/10.1007/s00158-013-0978-6	journal	August 2013
A sequential element rejection and admission (SERA) topology optimization code written in Matlab Ansola Loyola, Rubén; Querin, Osvaldo M.; Garaigordobil Jiménez, Alain Structural and Multidisciplinary Optimization, Vol. 58, Issue 3 https://doi.org/10.1007/s00158-018-1939-x	journal	February 2018
A stress-based topology optimization method for heterogeneous structures Conlan-Smith, Cian; James, Kai A. Structural and Multidisciplinary Optimization, Vol. 60, Issue 1 https://doi.org/10.1007/s00158-019-02207-9	journal	February 2019
TOuNN: Topology Optimization using Neural Networks Chandrasekhar, Aaditya; Suresh, Krishnan Structural and Multidisciplinary Optimization, Vol. 63, Issue 3 https://doi.org/10.1007/s00158-020-02748-4	journal	November 2020
A 99 line topology optimization code written in Matlab Sigmund, O. Structural and Multidisciplinary Optimization, Vol. 21, Issue 2 https://doi.org/10.1007/s001580050176	journal	April 2001
Developing strategic thinking Mason, Jef Long Range Planning, Vol. 19, Issue 3 https://doi.org/10.1016/0024-6301(86)90201-3	journal	June 1986
Generating optimal topologies in structural design using a homogenization method Bendsøe, Martin Philip; Kikuchi, Noboru Computer Methods in Applied Mechanics and Engineering, Vol. 71, Issue 2 https://doi.org/10.1016/0045-7825(88)90086-2	journal	November 1988
Chapter 13. Markovian Decision Processes Mathematics in Science and Engineering https://doi.org/10.1016/S0076-5392(08)61190-X	book	January 1977
Reward is enough Silver, David; Singh, Satinder; Precup, Doina Artificial Intelligence, Vol. 299 https://doi.org/10.1016/j.artint.2021.103535	journal	October 2021
Universal machine learning for topology optimization Chi, Heng; Zhang, Yuyu; Tang, Tsz Ling Elaine Computer Methods in Applied Mechanics and Engineering, Vol. 375 https://doi.org/10.1016/j.cma.2019.112739	journal	March 2021
Deep learning for topology optimization of 2D metamaterials Kollmann, Hunter T.; Abueidda, Diab W.; Koric, Seid Materials & Design, Vol. 196 https://doi.org/10.1016/j.matdes.2020.109098	journal	November 2020
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials Ma, Wei; Cheng, Feng; Liu, Yongmin ACS Nano, Vol. 12, Issue 6 https://doi.org/10.1021/acsnano.8b03569	journal	May 2018
Mastering the game of Go without human knowledge Silver, David; Schrittwieser, Julian; Simonyan, Karen Nature, Vol. 550, Issue 7676 https://doi.org/10.1038/nature24270	journal	October 2017
Big–deep–smart data in imaging for guiding materials design Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K. Nature Materials, Vol. 14, Issue 10 https://doi.org/10.1038/nmat4395	journal	September 2015
Mastering Atari, Go, chess and shogi by planning with a learned model Schrittwieser, Julian; Antonoglou, Ioannis; Hubert, Thomas Nature, Vol. 588, Issue 7839 https://doi.org/10.1038/s41586-020-03051-4	journal	December 2020
A graph placement methodology for fast chip design Mirhoseini, Azalia; Goldie, Anna; Yazgan, Mustafa Nature, Vol. 594, Issue 7862 https://doi.org/10.1038/s41586-021-03544-w	journal	June 2021
Highly accurate protein structure prediction with AlphaFold Jumper, John; Evans, Richard; Pritzel, Alexander Nature https://doi.org/10.1038/s41586-021-03819-2	journal	July 2021
Double-deep Q-learning to increase the efficiency of metasurface holograms Sajedian, Iman; Lee, Heon; Rho, Junsuk Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-47154-z	journal	July 2019
A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation Xue, Tianju; Beatson, Alex; Chiaramonte, Maurizio Soft Matter, Vol. 16, Issue 32 https://doi.org/10.1039/D0SM00488J	journal	January 2020
Engineering design: perspectives, challenges, and recent advances Liu, Shaofeng; Boyle, Iain M. Journal of Engineering Design, Vol. 20, Issue 1 https://doi.org/10.1080/09544820802670914	journal	February 2009
A data-driven investigation and estimation of optimal topologies under variable loading configurations Ulu, Erva; Zhang, Rusheng; Kara, Levent Burak Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 4, Issue 2 https://doi.org/10.1080/21681163.2015.1030775	journal	August 2015
A comparative study on stress and compliance based structural topology optimization Hailu Shimels, G.; Dereje Engida, W.; Fakhruldin Mohd, H. IOP Conference Series: Materials Science and Engineering, Vol. 241 https://doi.org/10.1088/1757-899X/241/1/012003	journal	October 2017
Importance of input data normalization for the application of neural networks to complex industrial problems Sola, J.; Sevilla, J. IEEE Transactions on Nuclear Science, Vol. 44, Issue 3 https://doi.org/10.1109/23.589532	journal	June 1997
Adaptive Exploration Strategy With Multi-Attribute Decision-Making for Reinforcement Learning Hu, Chunyang; Xu, Meng IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2973169	journal	January 2020
A Review of Machine Learning and Deep Learning Applications Shinde, Pramila P.; Shah, Seema 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) https://doi.org/10.1109/ICCUBEA.2018.8697857	conference	August 2018
A Multiscale Topology Optimization Methodology Based on Sequential Element Rejection–Admission and Boundary Element Evolvement Yang, Wenjia; Ho, S. L.; Fu, W. N. IEEE Transactions on Magnetics, Vol. 55, Issue 6 https://doi.org/10.1109/TMAG.2019.2895458	journal	June 2019
Reinforcement Learning: An Introduction Sutton, R. S.; Barto, A. G. IEEE Transactions on Neural Networks, Vol. 9, Issue 5 https://doi.org/10.1109/TNN.1998.712192	journal	September 1998
Topology optimization of continuum structures: A review* Eschenauer, Hans A.; Olhoff, Niels Applied Mechanics Reviews, Vol. 54, Issue 4 https://doi.org/10.1115/1.1388075	journal	July 2001
Machine Learning-Driven Real-Time Topology Optimization Under Moving Morphable Component-Based Framework Lei, Xin; Liu, Chang; Du, Zongliang Journal of Applied Mechanics, Vol. 86, Issue 1 https://doi.org/10.1115/1.4041319	journal	October 2018
A Case Study of Deep Reinforcement Learning for Engineering Design: Application to Microfluidic Devices for Flow Sculpting Lee, Xian Yeow; Balu, Aditya; Stoecklein, Daniel Journal of Mechanical Design, Vol. 141, Issue 11 https://doi.org/10.1115/1.4044397	journal	September 2019
Machine learning: Trends, perspectives, and prospects Jordan, M. I.; Mitchell, T. M. Science, Vol. 349, Issue 6245 https://doi.org/10.1126/science.aaa8415	journal	July 2015
A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges Arzate Cruz, Christian; Igarashi, Takeo DIS '20: Designing Interactive Systems Conference 2020, Proceedings of the 2020 ACM Designing Interactive Systems Conference https://doi.org/10.1145/3357236.3395525	conference	July 2020
Advancing non-convex and constrained learning: challenges and opportunities Yang, Tianbao AI Matters, Vol. 5, Issue 3 https://doi.org/10.1145/3362077.3362085	journal	December 2019
Molecular de-novo design through deep reinforcement learning Olivecrona, Marcus; Blaschke, Thomas; Engkvist, Ola Journal of Cheminformatics, Vol. 9, Issue 1 https://doi.org/10.1186/s13321-017-0235-x	journal	September 2017
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions Alzubaidi, Laith; Zhang, Jinglan; Humaidi, Amjad J. Journal of Big Data, Vol. 8, Issue 1 https://doi.org/10.1186/s40537-021-00444-8	journal	March 2021
Neural networks for topology optimization Sosnovik, Ivan; Oseledets, Ivan Russian Journal of Numerical Analysis and Mathematical Modelling, Vol. 34, Issue 4 https://doi.org/10.1515/rnam-2019-0018	journal	August 2019

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Title: “Deep reinforcement learning for engineering design through topology optimization of elementally discretized design domains”

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