Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control-Reference-Cited by-同舟云学术

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Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control

Published:2024-01 Issue: Volume:497 Page:112624
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Johnson Daniel,Fedkiw Ronald

Funder

Office of Naval Research Global

Office of Naval Research

Stanford University

Publisher

Elsevier BV

Subject

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

Reference85 articles.

1. Neural algorithm for solving differential equations;Lee;J. Comput. Phys.,1990

2. Dgm: a deep learning algorithm for solving partial differential equations;Sirignano;J. Comput. Phys.,2018

3. Hidden physics models: machine learning of nonlinear partial differential equations;Raissi;J. Comput. Phys.,2018

4. Deep uq: learning deep neural network surrogate models for high dimensional uncertainty quantification;Tripathy;J. Comput. Phys.,2018

5. Convpde-uq: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains;Winovich;J. Comput. Phys.,2019

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