Physics Informed Neural Networks for Simulating Radiative Transfer

25 September 2020

Papers citing "Physics Informed Neural Networks for Simulating Radiative Transfer"

25 / 25 papers shown

Title
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers Taniya Kapoor Abhishek Chandra Anastasios Stamou Stephen J Roberts 14 0 0 18 May 2025
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions Takeshi Koshizuka Issei Sato AI4CE 112 0 0 31 Jan 2025
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases Madison Cooley Varun Shankar Robert M. Kirby Shandian Zhe 32 2 0 04 Oct 2024
Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks Yianxia Qian Yongchao Zhang Suchuan Dong PINN 42 0 0 10 Jun 2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be? Jorge F. Urbán P. Stefanou José A. Pons PINN 45 6 0 07 May 2024
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations Hongyan Li Song Jiang Wenjun Sun Liwei Xu Guanyu Zhou 42 2 0 04 Mar 2024
An operator preconditioning perspective on training in physics-informed machine learning Tim De Ryck Florent Bonnet Siddhartha Mishra Emmanuel de Bezenac AI4CE 47 16 0 09 Oct 2023
Bayesian Reasoning for Physics Informed Neural Networks K. Graczyk Kornel Witkowski 40 0 0 25 Aug 2023
Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems G. S. Gusmão A. Medford PINN 22 8 0 12 Apr 2023
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time Y. Qian Yongchao Zhang Yuanfei Huang S. Dong PINN 26 1 0 22 Mar 2023
Error convergence and engineering-guided hyperparameter search of PINNs: towards optimized I-FENN performance Panos Pantidis Habiba Eldababy Christopher Miguel Tagle M. Mobasher 35 20 0 03 Mar 2023
A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations Haiyang Li Song Jiang Wenjun Sun Liwei Xu Guanyu Zhou 14 12 0 11 Dec 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 32 18 0 27 Oct 2022
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws Tim De Ryck Siddhartha Mishra Roberto Molinaro PINN 40 29 0 18 Jul 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 63 59 0 23 May 2022
Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions? Anastasis Kratsios Behnoosh Zamanlooy MLT 72 3 0 24 Apr 2022
Physics-constrained Unsupervised Learning of Partial Differential Equations using Meshes M. Michelis Robert K. Katzschmann AI4CE 35 1 0 30 Mar 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations Tim De Ryck Ameya Dilip Jagtap S. Mishra PINN 49 115 0 17 Mar 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 31 1,190 0 14 Jan 2022
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs Tim De Ryck Siddhartha Mishra PINN 26 100 0 28 Jun 2021
Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure Juntao Huang Yingda Cheng Andrew J. Christlieb L. Roberts AI4CE 23 26 0 12 May 2021
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 34 138 0 18 Apr 2021
Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification L. Scarabosio 16 9 0 18 Jan 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 35 146 0 22 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 186 763 0 13 Mar 2020