Constraint-Aware Neural Networks for Riemann Problems

29 April 2019

Papers citing "Constraint-Aware Neural Networks for Riemann Problems"

8 / 8 papers shown

Title
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 34 43 0 09 Jul 2024
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Javier Orera-Echeverria J. Ayensa-Jiménez Manuel Doblaré 25 1 0 07 Aug 2023
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control Dan Johnson Ronald Fedkiw AI4CE 31 2 0 21 Jun 2023
A comparison of PINN approaches for drift-diffusion equations on metric graphs J. Blechschmidt Jan-Frederik Pietschman Tom-Christian Riemer Martin Stoll M. Winkler 18 2 0 15 May 2022
Physics-informed neural networks for inverse problems in supersonic flows Ameya Dilip Jagtap Zhiping Mao Nikolaus Adams George Karniadakis PINN 20 201 0 23 Feb 2022
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors Dhruv V. Patel Deep Ray Assad A. Oberai AI4CE 19 37 0 06 Jul 2021
Structure-preserving neural networks Quercus Hernandez Alberto Badías D. González Francisco Chinesta Elías Cueto PINN 22 69 0 09 Apr 2020
Tensor Basis Gaussian Process Models of Hyperelastic Materials A. Frankel Reese E. Jones L. Swiler 11 41 0 23 Dec 2019