Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
2109.01050
Cited By
v1
v2 (latest)
Characterizing possible failure modes in physics-informed neural networks
2 September 2021
Aditi S. Krishnapriyan
A. Gholami
Shandian Zhe
Robert M. Kirby
Michael W. Mahoney
PINN
AI4CE
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Characterizing possible failure modes in physics-informed neural networks"
17 / 267 papers shown
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
International Conference on Machine Learning (ICML), 2022
D. Long
Liang Luo
Aditi S. Krishnapriyan
Robert M. Kirby
Shandian Zhe
Michael W. Mahoney
AI4CE
331
22
0
24 Feb 2022
Learning Physics-Informed Neural Networks without Stacked Back-propagation
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Di He
Shanda Li
Wen-Wu Shi
Xiaotian Gao
Jia Zhang
Jiang Bian
Liwei Wang
Tie-Yan Liu
DiffM
PINN
AI4CE
253
32
0
18 Feb 2022
Learning continuous models for continuous physics
Communications Physics (Commun. Phys.), 2022
Aditi S. Krishnapriyan
A. Queiruga
N. Benjamin Erichson
Michael W. Mahoney
AI4CE
363
39
0
17 Feb 2022
When Do Flat Minima Optimizers Work?
Neural Information Processing Systems (NeurIPS), 2022
Jean Kaddour
Linqing Liu
Ricardo M. A. Silva
Matt J. Kusner
ODL
519
85
0
01 Feb 2022
Numerical Approximation of Partial Differential Equations by a Variable Projection Method with Artificial Neural Networks
Computer Methods in Applied Mechanics and Engineering (CMAME), 2022
S. Dong
Jielin Yang
225
20
0
24 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Journal of Scientific Computing (J. Sci. Comput.), 2022
S. Cuomo
Vincenzo Schiano Di Cola
F. Giampaolo
G. Rozza
Maizar Raissi
F. Piccialli
PINN
502
1,820
0
14 Jan 2022
Learning finite difference methods for reaction-diffusion type equations with FCNN
Computers and Mathematics with Applications (CMA), 2022
Yongho Kim
Yongho Choi
73
8
0
04 Jan 2022
Learning from learning machines: a new generation of AI technology to meet the needs of science
L. Pion-Tonachini
K. Bouchard
Héctor García Martín
S. Peisert
W. B. Holtz
...
Rick L. Stevens
Mark Anderson
Ken Kreutz-Delgado
Michael W. Mahoney
James B. Brown
211
9
0
27 Nov 2021
On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements
S. Dong
Jielin Yang
219
61
0
27 Oct 2021
Fast PDE-constrained optimization via self-supervised operator learning
Sizhuang He
Mohamed Aziz Bhouri
P. Perdikaris
200
35
0
25 Oct 2021
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
Biswajit Khara
Aditya Balu
Ameya Joshi
Soumik Sarkar
Chinmay Hegde
A. Krishnamurthy
Baskar Ganapathysubramanian
242
24
0
04 Oct 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Rui Wang
Rose Yu
AI4CE
PINN
490
82
0
02 Jul 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
IEEE Access (IEEE Access), 2021
Franz M. Rohrhofer
S. Posch
C. Gößnitzer
Bernhard C. Geiger
PINN
408
47
0
03 May 2021
One-shot learning for solution operators of partial differential equations
Nature Communications (Nat Commun), 2021
Priya Kasimbeg
Haiyang He
Rishikesh Ranade
Jay Pathak
Lu Lu
AI4CE
372
18
0
06 Apr 2021
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
538
171
0
22 Dec 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
ACM Computing Surveys (ACM CSUR), 2020
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
663
525
0
10 Mar 2020
Hamiltonian neural networks for solving equations of motion
Physical Review E (PRE), 2020
M. Mattheakis
David Sondak
Akshunna S. Dogra
P. Protopapas
463
82
0
29 Jan 2020
Previous
1
2
3
4
5
6
