Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Author:

Linka Kevin,Schäfer Amelie,Meng Xuhui,Zou Zongren,Karniadakis George Em,Kuhl EllenORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference34 articles.

1. Multiscale modeling meets machine learning: What can we learn?;Peng;Arch. Comput. Methods Eng.,2021

2. Physics-informed machine learning;Karniadakis;Nat. Rev. Phys.,2021

3. Integrating machine learning and multiscale modeling: Perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences;Alber;Npj Digit. Med.,2019

4. Physics informed deep learning: Data-driven solutions of nonlinear partial differential equations;Raissi,2017

5. Multistep neural networks for data-driven discovery of nonlinear dynamical systems;Raissi,2018

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