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2102.13037
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SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs
25 February 2021
A. A. Ramabathiran
P. Ramachandran
PINN
AI4CE
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Papers citing
"SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs"
10 / 10 papers shown
Title
Harnessing physics-informed operators for high-dimensional reliability analysis problems
N Navaneeth
Tushar
Souvik Chakraborty
AI4CE
32
0
0
07 Sep 2024
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
10
0
0
18 Jan 2024
Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs
A. A. Ramabathiran
P. Ramachandran
AI4CE
9
0
0
01 Jul 2022
Learning Stochastic Dynamics with Statistics-Informed Neural Network
Yuanran Zhu
Yunhao Tang
Changho Kim
11
18
0
24 Feb 2022
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
Biswajit Khara
Aditya Balu
Ameya Joshi
S. Sarkar
C. Hegde
A. Krishnamurthy
Baskar Ganapathysubramanian
19
19
0
04 Oct 2021
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
N. Sukumar
Ankit Srivastava
PINN
AI4CE
36
239
0
17 Apr 2021
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
39
489
0
09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Sifan Wang
Hanwen Wang
P. Perdikaris
129
435
0
18 Dec 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
Ziqi Liu
Wei Cai
Zhi-Qin John Xu
AI4CE
125
122
0
22 Jul 2020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
E. Samaniego
C. Anitescu
S. Goswami
Vien Minh Nguyen-Thanh
Hongwei Guo
Khader M. Hamdia
Timon Rabczuk
X. Zhuang
PINN
AI4CE
145
1,333
0
27 Aug 2019
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