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2210.07880
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Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations
14 October 2022
Alexander New
B. Eng
A. Timm
A. Gearhart
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Papers citing
"Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations"
5 / 5 papers shown
Title
Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
Vinay Sharma
Olga Fink
AI4CE
45
1
0
13 Jan 2025
Data-efficient operator learning for solving high Mach number fluid flow problems
Noah Ford
Victor J. Leon
Honest Mrema
Jeffrey Gilbert
Alexander New
AI4CE
19
0
0
28 Nov 2023
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
177
758
0
13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
E. Kharazmi
Zhongqiang Zhang
George Karniadakis
125
508
0
11 Mar 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
88
387
0
10 Mar 2020
1