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Physics-Informed Machine Learning: A Survey on Problems, Methods and
  Applications

Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications

15 November 2022
Zhongkai Hao
Songming Liu
Yichi Zhang
Chengyang Ying
Yao Feng
Hang Su
Jun Zhu
    PINN
    AI4CE
ArXivPDFHTML

Papers citing "Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications"

35 / 35 papers shown
Title
Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems
Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems
Josue N. Rivera
Dengfeng Sun
28
0
0
02 Nov 2024
Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks
R. Mattey
Susanta Ghosh
AI4CE
40
1
0
09 May 2024
Neural Parameter Regression for Explicit Representations of PDE Solution
  Operators
Neural Parameter Regression for Explicit Representations of PDE Solution Operators
Konrad Mundinger
Max Zimmer
S. Pokutta
44
0
0
19 Mar 2024
From Spectra to Biophysical Insights: End-to-End Learning with a Biased
  Radiative Transfer Model
From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model
Yihang She
Clement Atzberger
Andrew Blake
Srinivasan Keshav
16
0
0
05 Mar 2024
Discovering Artificial Viscosity Models for Discontinuous Galerkin
  Approximation of Conservation Laws using Physics-Informed Machine Learning
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
Matteo Caldana
P. Antonietti
Luca Dede'
AI4CE
PINN
27
1
0
26 Feb 2024
Physics-Informed Computer Vision: A Review and Perspectives
Physics-Informed Computer Vision: A Review and Perspectives
C. Banerjee
Kien Nguyen
Clinton Fookes
G. Karniadakis
PINN
AI4CE
34
28
0
29 May 2023
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for
  Meta-Learning
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Jun Shu
Xiang Yuan
Deyu Meng
Zongben Xu
28
4
0
13 May 2023
Probing optimisation in physics-informed neural networks
Probing optimisation in physics-informed neural networks
Nayara Fonseca
V. Guidetti
Will Trojak
31
1
0
27 Mar 2023
Equivariant Energy-Guided SDE for Inverse Molecular Design
Equivariant Energy-Guided SDE for Inverse Molecular Design
Fan Bao
Min Zhao
Zhongkai Hao
Pei‐Yun Li
Chongxuan Li
Jun Zhu
DiffM
187
63
0
30 Sep 2022
Fourier Neural Operator with Learned Deformations for PDEs on General
  Geometries
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
Zong-Yi Li
Daniel Zhengyu Huang
Burigede Liu
Anima Anandkumar
AI4CE
116
249
0
11 Jul 2022
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object
  Interactions
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
Sammy Christen
Muhammed Kocabas
Emre Aksan
Jemin Hwangbo
Jie Song
Otmar Hilliges
108
104
0
01 Dec 2021
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
305
7,434
0
11 Nov 2021
Solving PDE-constrained Control Problems Using Operator Learning
Solving PDE-constrained Control Problems Using Operator Learning
Rakhoon Hwang
Jae Yong Lee
J. Shin
H. Hwang
AI4CE
114
43
0
09 Nov 2021
Improved architectures and training algorithms for deep operator
  networks
Improved architectures and training algorithms for deep operator networks
Sifan Wang
Hanwen Wang
P. Perdikaris
AI4CE
49
105
0
04 Oct 2021
Multiwavelet-based Operator Learning for Differential Equations
Multiwavelet-based Operator Learning for Differential Equations
Gaurav Gupta
Xiongye Xiao
P. Bogdan
126
200
0
28 Sep 2021
Arbitrary-Depth Universal Approximation Theorems for Operator Neural
  Networks
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks
Annan Yu
Chloe Becquey
Diana Halikias
Matthew Esmaili Mallory
Alex Townsend
59
8
0
23 Sep 2021
Physics-based Deep Learning
Physics-based Deep Learning
Nils Thuerey
Philipp Holl
P. Holl
Patrick Schnell
Felix Trost
Kiwon Um
P. Schnell
F. Trost
PINN
AI4CE
53
92
0
11 Sep 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable
  domain decomposition approach for solving differential equations
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Benjamin Moseley
Andrew Markham
T. Nissen‐Meyer
PINN
45
209
0
16 Jul 2021
Meta-learning PINN loss functions
Meta-learning PINN loss functions
Apostolos F. Psaros
Kenji Kawaguchi
George Karniadakis
PINN
41
97
0
12 Jul 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
174
1,104
0
27 Apr 2021
Efficient training of physics-informed neural networks via importance
  sampling
Efficient training of physics-informed neural networks via importance sampling
M. A. Nabian
R. J. Gladstone
Hadi Meidani
DiffM
PINN
71
222
0
26 Apr 2021
Parallel Physics-Informed Neural Networks via Domain Decomposition
Parallel Physics-Informed Neural Networks via Domain Decomposition
K. Shukla
Ameya Dilip Jagtap
George Karniadakis
PINN
101
274
0
20 Apr 2021
Physics-informed neural networks with hard constraints for inverse
  design
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
494
0
09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to
  solving multi-scale PDEs with physics-informed neural networks
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
131
438
0
18 Dec 2020
Fourier Neural Operator for Parametric Partial Differential Equations
Fourier Neural Operator for Parametric Partial Differential Equations
Zong-Yi Li
Nikola B. Kovachki
Kamyar Azizzadenesheli
Burigede Liu
K. Bhattacharya
Andrew M. Stuart
Anima Anandkumar
AI4CE
205
2,282
0
18 Oct 2020
Multi-scale Deep Neural Network (MscaleDNN) for Solving
  Poisson-Boltzmann Equation in Complex Domains
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
Ziqi Liu
Wei Cai
Zhi-Qin John Xu
AI4CE
211
122
0
22 Jul 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain
  Decomposition
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
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
91
387
0
10 Mar 2020
Lagrangian Neural Networks
Lagrangian Neural Networks
M. Cranmer
S. Greydanus
Stephan Hoyer
Peter W. Battaglia
D. Spergel
S. Ho
PINN
130
424
0
10 Mar 2020
Measurement-based adaptation protocol with quantum reinforcement
  learning in a Rigetti quantum computer
Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
Julio Olivares-Sánchez
J. Casanova
Enrique Solano
L. Lamata
24
36
0
19 Nov 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph
  Generation
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Jiaxuan You
Bowen Liu
Rex Ying
Vijay S. Pande
J. Leskovec
GNN
203
885
0
07 Jun 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin
Regina Barzilay
Tommi Jaakkola
224
1,338
0
12 Feb 2018
PointNet: Deep Learning on Point Sets for 3D Classification and
  Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. Qi
Hao Su
Kaichun Mo
Leonidas J. Guibas
3DH
3DPC
3DV
PINN
222
14,099
0
02 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
278
1,400
0
01 Dec 2016
Stochastic Gradient Descent for Non-smooth Optimization: Convergence
  Results and Optimal Averaging Schemes
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir
Tong Zhang
101
570
0
08 Dec 2012
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