Entropy-based closure for probabilistic learning on manifolds

Soize, C.; Ghanem, R.; Safta, C.; Huan, X.; Vane, Z. P.; Oefelein, J.; Lacaze, G.; Najm, H. N.; Tang, Q.; Chen, X.

doi:10.1016/j.jcp.2018.12.029

Title: Entropy-based closure for probabilistic learning on manifolds

Journal Article · Wed Mar 27 00:00:00 EDT 2019 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2018.12.029· OSTI ID:1526161

Soize, C. ^[1]; Ghanem, R. ^[2]; Safta, C. ^[3]; Huan, X. ^[3]; Vane, Z. P. ^[3]; Oefelein, J. ^[3]; Lacaze, G. ^[3]; Najm, H. N. ^[3]; Tang, Q. ^[4]; Chen, X. ^[4]

Univ. Paris, Marne-La-Vallee (France)
Univ. of Southern California, Los Angeles, CA (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Defense Advanced Research Projects Agency (DARPA)

Grant/Contract Number:: AC52-07NA27344; AC04-94AL85000; AC02-06CH11357

OSTI ID:: 1526161

Alternate ID(s):: OSTI ID: 1529282; OSTI ID: 1775899

Report Number(s):: LLNL-JRNL-744191; SAND-2018-0462J; 899147

Journal Information:: Journal of Computational Physics, Vol. 388, Issue C; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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References (27)

Data-driven probability concentration and sampling on manifold Soize, C.; Ghanem, R. Journal of Computational Physics, Vol. 321 https://doi.org/10.1016/j.jcp.2016.05.044	journal	September 2016
Polynomial chaos representation of databases on manifolds Soize, C.; Ghanem, R. Journal of Computational Physics, Vol. 335 https://doi.org/10.1016/j.jcp.2017.01.031	journal	April 2017
Sparse regression learning by aggregation and Langevin Monte-Carlo Dalalyan, A. S.; Tsybakov, A. B. Journal of Computer and System Sciences, Vol. 78, Issue 5 https://doi.org/10.1016/j.jcss.2011.12.023	journal	September 2012
Probabilistic machine learning and artificial intelligence Ghahramani, Zoubin Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14541	journal	May 2015
Statistical learning and selective inference Taylor, Jonathan; Tibshirani, Robert J. Proceedings of the National Academy of Sciences, Vol. 112, Issue 25 https://doi.org/10.1073/pnas.1507583112	journal	June 2015
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
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
On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning Byrd, Richard H.; Chin, Gillian M.; Neveitt, Will SIAM Journal on Optimization, Vol. 21, Issue 3 https://doi.org/10.1137/10079923X	journal	July 2011
Constrained optimization in expensive simulation: Novel approach Kleijnen, Jack P. C.; Beers, Wim van; Nieuwenhuyse, Inneke van European Journal of Operational Research, Vol. 202, Issue 1 https://doi.org/10.1016/j.ejor.2009.05.002	journal	April 2010
Bayesian Optimization in a Billion Dimensions via Random Embeddings Wang, Ziyu; Hutter, Frank; Zoghi, Masrour Journal of Artificial Intelligence Research, Vol. 55 https://doi.org/10.1613/jair.4806	journal	January 2016
Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs Xie, Jing; Frazier, Peter I.; Chick, Stephen E. Operations Research, Vol. 64, Issue 2 https://doi.org/10.1287/opre.2016.1480	journal	April 2016
Sequential Optimization and Reliability Assessment Method for Efficient Probabilistic Design Du, Xiaoping; Chen, Wei Journal of Mechanical Design, Vol. 126, Issue 2 https://doi.org/10.1115/1.1649968	journal	March 2004
Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles Yao, Wen; Chen, Xiaoqian; Luo, Wencai Progress in Aerospace Sciences, Vol. 47, Issue 6 https://doi.org/10.1016/j.paerosci.2011.05.001	journal	August 2011
Probabilistic nonconvex constrained optimization with fixed number of function evaluations: Probabilistic nonconvex constrained optimization with fixed number of function evaluations Ghanem, Roger; Soize, Christian International Journal for Numerical Methods in Engineering, Vol. 113, Issue 4 https://doi.org/10.1002/nme.5632	journal	September 2017
Optimal Well-Placement Using Probabilistic Learning Ghanem, Roger; Soize, Christian; Thimmisetty, Charanraj Data-Enabled Discovery and Applications, Vol. 2, Issue 1 https://doi.org/10.1007/s41688-017-0014-x	journal	January 2018
Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem Thimmisetty, Charanraj; Tsilifis, Panagiotis; Ghanem, Roger Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 31, Issue 3 https://doi.org/10.1017/S0890060417000166	journal	August 2017
The atmospheric hydrologic cycle in the ACME v0.3 model Terai, Christopher R.; Caldwell, Peter M.; Klein, Stephen A. Climate Dynamics, Vol. 50, Issue 9-10 https://doi.org/10.1007/s00382-017-3803-x	journal	July 2017
Polynomial Chaos Expansion of a Multimodal Random Vector Soize, C. SIAM/ASA Journal on Uncertainty Quantification, Vol. 3, Issue 1 https://doi.org/10.1137/140968495	journal	January 2015
Construction of probability distributions in high dimension using the maximum entropy principle: Applications to stochastic processes, random fields and random matrices Soize, Christian International Journal for Numerical Methods in Engineering, Vol. 76, Issue 10 https://doi.org/10.1002/nme.2385	journal	December 2008
Riemann manifold Langevin and Hamiltonian Monte Carlo methods: Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods Girolami, Mark; Calderhead, Ben Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 73, Issue 2 https://doi.org/10.1111/j.1467-9868.2010.00765.x	journal	March 2011
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps Coifman, R. R.; Lafon, S.; Lee, A. B. Proceedings of the National Academy of Sciences, Vol. 102, Issue 21 https://doi.org/10.1073/pnas.0500334102	journal	May 2005
Diffusion maps Coifman, Ronald R.; Lafon, Stéphane Applied and Computational Harmonic Analysis, Vol. 21, Issue 1 https://doi.org/10.1016/j.acha.2006.04.006	journal	July 2006
Information Theory and Statistical Mechanics Jaynes, E. T. Physical Review, Vol. 106, Issue 4 https://doi.org/10.1103/PhysRev.106.620	journal	May 1957
Information Theory and Statistical Mechanics. II Jaynes, E. T. Physical Review, Vol. 108, Issue 2 https://doi.org/10.1103/PhysRev.108.171	journal	October 1957
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
Monte Carlo sampling-based methods for stochastic optimization Homem-de-Mello, Tito; Bayraksan, Güzin Surveys in Operations Research and Management Science, Vol. 19, Issue 1 https://doi.org/10.1016/j.sorms.2014.05.001	journal	January 2014
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy Balcan, Maria-Florina; Feldman, Vitaly Carnegie Mellon University https://doi.org/10.1184/r1/6476360	text	January 2014

Cited By (3)

Physics‐constrained non‐Gaussian probabilistic learning on manifolds Soize, Christian; Ghanem, Roger International Journal for Numerical Methods in Engineering, Vol. 121, Issue 1 https://doi.org/10.1002/nme.6202	journal	September 2019
Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds Ghanem, R. G.; Soize, C.; Safta, C. Journal of Computational Physics, Vol. 399 https://doi.org/10.1016/j.jcp.2019.108930	journal	December 2019
Machine learning for detecting structural changes from dynamic monitoring using the probabilistic learning on manifolds Soize, Christian; Orcesi, André Structure and Infrastructure Engineering, Vol. 17, Issue 10 https://doi.org/10.1080/15732479.2020.1811991	journal	September 2020

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