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Physics and Equality Constrained Artificial Neural Networks: Application
  to Forward and Inverse Problems with Multi-fidelity Data Fusion

Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion

30 September 2021
S. Basir
Inanc Senocak
    PINN
    AI4CE
ArXivPDFHTML

Papers citing "Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion"

25 / 25 papers shown
Title
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks
D. Veerababu
Prasanta K. Ghosh
83
0
0
26 Mar 2025
Estimation of the Acoustic Field in a Uniform Duct with Mean Flow using Neural Networks
Estimation of the Acoustic Field in a Uniform Duct with Mean Flow using Neural Networks
D. Veerababu
Prasanta K. Ghosh
AI4CE
59
0
0
25 Mar 2025
SPINN: Sparse, Physics-based, and partially Interpretable Neural
  Networks for PDEs
SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs
A. A. Ramabathiran
P. Ramachandran
PINN
AI4CE
66
77
0
25 Feb 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
70
508
0
09 Feb 2021
Training neural networks under physical constraints using a stochastic
  augmented Lagrangian approach
Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
A. Dener
M. Miller
R. Churchill
T. Munson
Choong-Seock Chang
18
22
0
15 Sep 2020
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention
  Mechanism
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
L. McClenny
U. Braga-Neto
PINN
58
452
0
07 Sep 2020
Hierarchical Deep Learning of Multiscale Differential Equation
  Time-Steppers
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
Yuying Liu
N. Kutz
Steven L. Brunton
AI4TS
38
76
0
22 Aug 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
242
42,038
0
03 Dec 2019
Machine Learning for Fluid Mechanics
Machine Learning for Fluid Mechanics
Steven Brunton
B. R. Noack
Petros Koumoutsakos
AI4CE
PINN
72
2,105
0
27 May 2019
Machine learning in cardiovascular flows modeling: Predicting arterial
  blood pressure from non-invasive 4D flow MRI data using physics-informed
  neural networks
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Georgios Kissas
Yibo Yang
E. Hwuang
W. Witschey
John A. Detre
P. Perdikaris
AI4CE
101
368
0
13 May 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate
  Modeling and Uncertainty Quantification without Labeled Data
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Yinhao Zhu
N. Zabaras
P. Koutsourelakis
P. Perdikaris
PINN
AI4CE
66
860
0
18 Jan 2019
Analysis of the Generalization Error: Empirical Risk Minimization over
  Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the
  Numerical Approximation of Black-Scholes Partial Differential Equations
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations
Julius Berner
Philipp Grohs
Arnulf Jentzen
47
182
0
09 Sep 2018
A proof that artificial neural networks overcome the curse of
  dimensionality in the numerical approximation of Black-Scholes partial
  differential equations
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
Philipp Grohs
F. Hornung
Arnulf Jentzen
Philippe von Wurstemberger
36
169
0
07 Sep 2018
Deep Learning of Vortex Induced Vibrations
Deep Learning of Vortex Induced Vibrations
M. Raissi
Zhicheng Wang
M. Triantafyllou
George Karniadakis
AI4CE
36
373
0
26 Aug 2018
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial
  Differential Equations
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
M. Raissi
PINN
AI4CE
93
751
0
20 Jan 2018
The Deep Ritz method: A deep learning-based numerical algorithm for
  solving variational problems
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems
E. Weinan
Ting Yu
97
1,373
0
30 Sep 2017
DGM: A deep learning algorithm for solving partial differential
  equations
DGM: A deep learning algorithm for solving partial differential equations
Justin A. Sirignano
K. Spiliopoulos
AI4CE
61
2,048
0
24 Aug 2017
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
331
18,300
0
27 May 2016
Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
281
10,149
0
16 Mar 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.4K
192,638
0
10 Dec 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
806
149,474
0
22 Dec 2014
Sequence to Sequence Learning with Neural Networks
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever
Oriol Vinyals
Quoc V. Le
AIMat
275
20,491
0
10 Sep 2014
Neural Machine Translation by Jointly Learning to Align and Translate
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau
Kyunghyun Cho
Yoshua Bengio
AIMat
383
27,205
0
01 Sep 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
171
16,311
0
30 Apr 2014
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
108
6,619
0
22 Dec 2012
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