Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems-Reference-Cited by-同舟云学术

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

Published:2020-10 Issue: Volume:419 Page:109673
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Kaltenbach Sebastian^ORCID,Koutsourelakis Phaedon-Stelios^ORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modeling and Simulation,Numerical Analysis

Reference68 articles.

1. Extracting macroscopic dynamics: model problems and algorithms;Givon;Nonlinearity,2004

2. Biophysics: Searching for Principles;Bialek,2012

3. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences;Alber;NPJ Digit. Med.,2019

4. Probabilistic machine learning and artificial intelligence;Ghahramani;Nature,2015

5. Deep learning;LeCun;Nature,2015

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media;Computer Methods in Applied Mechanics and Engineering;2024-12

2. Determination of Gas–Oil minimum miscibility pressure for impure CO2 through optimized machine learning models;Geoenergy Science and Engineering;2024-11

3. Predicting and explaining nonlinear material response using deep physically guided neural networks with internal variables;Mathematics and Mechanics of Solids;2024-07-26

4. Information theoretic clustering for coarse-grained modeling of non-equilibrium gas dynamics;Journal of Computational Physics;2024-06

5. Physics-Aware Neural Implicit Solvers for Multiscale, Parametric Pdes with Applications in Heterogeneous Media;2024