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DeepXDE: A deep learning library for solving differential equations
v1v2 (latest)

DeepXDE: A deep learning library for solving differential equations

10 July 2019
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "DeepXDE: A deep learning library for solving differential equations"

50 / 484 papers shown
Title
IDRLnet: A Physics-Informed Neural Network Library
IDRLnet: A Physics-Informed Neural Network Library
Wei Peng
Jun Zhang
Weien Zhou
Xiaoyu Zhao
Wen Yao
Xiaoqian Chen
PINNAI4CE
84
16
0
09 Jul 2021
Cell-average based neural network method for hyperbolic and parabolic
  partial differential equations
Cell-average based neural network method for hyperbolic and parabolic partial differential equations
Changxin Qiu
Jue Yan
64
10
0
02 Jul 2021
Error analysis for physics informed neural networks (PINNs)
  approximating Kolmogorov PDEs
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs
Tim De Ryck
Siddhartha Mishra
PINN
99
103
0
28 Jun 2021
Legendre Deep Neural Network (LDNN) and its application for
  approximation of nonlinear Volterra Fredholm Hammerstein integral equations
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra Fredholm Hammerstein integral equations
Z. Hajimohammadi
Kourosh Parand
A. Ghodsi
51
5
0
27 Jun 2021
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving
  Spatiotemporal PDEs
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs
Pu Ren
Chengping Rao
Yang Liu
Jianxun Wang
Hao Sun
DiffMAI4CE
136
206
0
26 Jun 2021
Lagrangian dual framework for conservative neural network solutions of
  kinetic equations
Lagrangian dual framework for conservative neural network solutions of kinetic equations
H. Hwang
Hwijae Son
74
8
0
23 Jun 2021
Approximation capabilities of measure-preserving neural networks
Approximation capabilities of measure-preserving neural networks
Aiqing Zhu
Pengzhan Jin
Yifa Tang
84
8
0
21 Jun 2021
Long-time integration of parametric evolution equations with
  physics-informed DeepONets
Long-time integration of parametric evolution equations with physics-informed DeepONets
Sizhuang He
P. Perdikaris
AI4CE
89
122
0
09 Jun 2021
Choose a Transformer: Fourier or Galerkin
Choose a Transformer: Fourier or Galerkin
Shuhao Cao
90
256
0
31 May 2021
TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems
TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems
Hai V. Nguyen
T. Bui-Thanh
PINNAI4CE
48
10
0
25 May 2021
Learning Green's Functions of Linear Reaction-Diffusion Equations with
  Application to Fast Numerical Solver
Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver
Yuankai Teng
Xiaoping Zhang
Zhu Wang
L. Ju
90
14
0
23 May 2021
BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed
  deep learning
BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learning
Hanfeng Zhai
Quan Zhou
G. Hu
PINNAI4CE
63
16
0
15 May 2021
On the reproducibility of fully convolutional neural networks for
  modeling time-space evolving physical systems
On the reproducibility of fully convolutional neural networks for modeling time-space evolving physical systems
Wagner Gonçalves Pinto
Antonio Alguacil
Michaël Bauerheim
41
2
0
12 May 2021
ReLU Deep Neural Networks from the Hierarchical Basis Perspective
ReLU Deep Neural Networks from the Hierarchical Basis Perspective
Juncai He
Lin Li
Jinchao Xu
AI4CE
59
30
0
10 May 2021
Data-driven discovery of Green's functions with human-understandable
  deep learning
Data-driven discovery of Green's functions with human-understandable deep learning
Nicolas Boullé
Christopher Earls
Alex Townsend
PINNAI4CE
60
60
0
01 May 2021
Deep learning neural networks for the third-order nonlinear Schrodinger
  equation: Solitons, breathers, and rogue waves
Deep learning neural networks for the third-order nonlinear Schrodinger equation: Solitons, breathers, and rogue waves
Zijian Zhou
Zhenya Yan
137
34
0
30 Apr 2021
Exact imposition of boundary conditions with distance functions in
  physics-informed deep neural networks
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks
N. Sukumar
Ankit Srivastava
PINNAI4CE
157
255
0
17 Apr 2021
Applications of physics-informed scientific machine learning in
  subsurface science: A survey
Applications of physics-informed scientific machine learning in subsurface science: A survey
A. Sun
H. Yoon
C. Shih
Zhi Zhong
AI4CE
82
11
0
10 Apr 2021
Conditional physics informed neural networks
Conditional physics informed neural networks
A. Kovacs
L. Exl
Alexander Kornell
J. Fischbacher
Markus Hovorka
...
N. Sakuma
Akihito Kinoshita
T. Shoji
A. Kato
T. Schrefl
PINN
70
44
0
06 Apr 2021
One-shot learning for solution operators of partial differential
  equations
One-shot learning for solution operators of partial differential equations
Priya Kasimbeg
Haiyang He
Rishikesh Ranade
Jay Pathak
Lu Lu
AI4CE
95
12
0
06 Apr 2021
Distributional Offline Continuous-Time Reinforcement Learning with
  Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)
Distributional Offline Continuous-Time Reinforcement Learning with Neural Physics-Informed PDEs (SciPhy RL for DOCTR-L)
I. Halperin
OffRL
70
7
0
02 Apr 2021
Deep Learning of Conjugate Mappings
Deep Learning of Conjugate Mappings
J. Bramburger
S. Patterson
J. Nathan Kutz
75
15
0
01 Apr 2021
dNNsolve: an efficient NN-based PDE solver
dNNsolve: an efficient NN-based PDE solver
V. Guidetti
F. Muia
Y. Welling
A. Westphal
50
6
0
15 Mar 2021
The Old and the New: Can Physics-Informed Deep-Learning Replace
  Traditional Linear Solvers?
The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?
Stefano Markidis
PINN
75
193
0
12 Mar 2021
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
PINNAI4CE
144
80
0
25 Feb 2021
NTopo: Mesh-free Topology Optimization using Implicit Neural
  Representations
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
Jonas Zehnder
Yue Li
Stelian Coros
B. Thomaszewski
AI4CE
80
61
0
22 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
102
522
0
09 Feb 2021
Inferring incompressible two-phase flow fields from the interface motion
  using physics-informed neural networks
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks
Aaron B. Buhendwa
Stefan Adami
Nikolaus A. Adams
AI4CEPINN
50
35
0
25 Jan 2021
A Taylor Based Sampling Scheme for Machine Learning in Computational
  Physics
A Taylor Based Sampling Scheme for Machine Learning in Computational Physics
Paul Novello
Gaël Poëtte
D. Lugato
P. Congedo
PINNAI4CE
127
0
0
20 Jan 2021
Deep neural network surrogates for non-smooth quantities of interest in
  shape uncertainty quantification
Deep neural network surrogates for non-smooth quantities of interest in shape uncertainty quantification
L. Scarabosio
84
9
0
18 Jan 2021
Data-driven discovery of multiscale chemical reactions governed by the
  law of mass action
Data-driven discovery of multiscale chemical reactions governed by the law of mass action
Juntao Huang
Y. Zhou
W. Yong
52
6
0
17 Jan 2021
Data-driven peakon and periodic peakon travelling wave solutions of some
  nonlinear dispersive equations via deep learning
Data-driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning
Li Wang
Zhenya Yan
115
48
0
12 Jan 2021
Constrained Block Nonlinear Neural Dynamical Models
Constrained Block Nonlinear Neural Dynamical Models
Elliott Skomski
Soumya Vasisht
Colby Wight
Aaron Tuor
Ján Drgoňa
D. Vrabie
AI4CE
112
16
0
06 Jan 2021
Learning emergent PDEs in a learned emergent space
Learning emergent PDEs in a learned emergent space
Felix P. Kemeth
Tom S. Bertalan
Thomas Thiem
Felix Dietrich
S. Moon
C. Laing
Ioannis G. Kevrekidis
AI4CE
34
7
0
23 Dec 2020
An overview on deep learning-based approximation methods for partial
  differential equations
An overview on deep learning-based approximation methods for partial differential equations
C. Beck
Martin Hutzenthaler
Arnulf Jentzen
Benno Kuckuck
119
153
0
22 Dec 2020
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework
O. Hennigh
S. Narasimhan
M. A. Nabian
Akshay Subramaniam
Kaustubh Tangsali
M. Rietmann
J. Ferrandis
Wonmin Byeon
Z. Fang
S. Choudhry
PINNAI4CE
146
130
0
14 Dec 2020
Algorithmically-Consistent Deep Learning Frameworks for Structural
  Topology Optimization
Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
Jaydeep Rade
Aditya Balu
Ethan Herron
Jay Pathak
Rishikesh Ranade
Soumik Sarkar
A. Krishnamurthy
54
44
0
09 Dec 2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing
  Process of Composite-Tool Systems During Manufacture
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
S. Niaki
E. Haghighat
Trevor Campbell
Xinglong Li
R. Vaziri
AI4CE
142
211
0
27 Nov 2020
On the application of Physically-Guided Neural Networks with Internal
  Variables to Continuum Problems
On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems
J. Ayensa-Jiménez
M. H. Doweidar
J. A. Sanz-Herrera
Manuel Doblaré
33
1
0
23 Nov 2020
MGIC: Multigrid-in-Channels Neural Network Architectures
MGIC: Multigrid-in-Channels Neural Network Architectures
Moshe Eliasof
Jonathan Ephrath
Lars Ruthotto
Eran Treister
107
8
0
17 Nov 2020
Texture image classification based on a pseudo-parabolic diffusion model
Texture image classification based on a pseudo-parabolic diffusion model
Jardel Vieira
E. Abreu
J. Florindo
DiffM
38
7
0
14 Nov 2020
Efficient nonlinear manifold reduced order model
Efficient nonlinear manifold reduced order model
Youngkyu Kim
Youngsoo Choi
David Widemann
T. Zohdi
74
43
0
13 Nov 2020
Physics-constrained Deep Learning of Multi-zone Building Thermal
  Dynamics
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
Ján Drgoňa
Aaron Tuor
V. Chandan
D. Vrabie
AI4CE
89
121
0
11 Nov 2020
Physics-informed Neural-Network Software for Molecular Dynamics
  Applications
Physics-informed Neural-Network Software for Molecular Dynamics Applications
Taufeq Mohammed Razakh
Beibei Wang
Shane Jackson
R. Kalia
A. Nakano
K. Nomura
P. Vashishta
PINN
66
11
0
06 Nov 2020
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Frequency-compensated PINNs for Fluid-dynamic Design Problems
Tongtao Zhang
Biswadip Dey
P. Kakkar
A. Dasgupta
Amit Chakraborty
PINNAI4CE
52
9
0
03 Nov 2020
Exponential ReLU Neural Network Approximation Rates for Point and Edge
  Singularities
Exponential ReLU Neural Network Approximation Rates for Point and Edge Singularities
C. Marcati
J. Opschoor
P. Petersen
Christoph Schwab
79
30
0
23 Oct 2020
Data-driven Identification of 2D Partial Differential Equations using
  extracted physical features
Data-driven Identification of 2D Partial Differential Equations using extracted physical features
Kazem Meidani
A. Farimani
64
17
0
20 Oct 2020
Living in the Physics and Machine Learning Interplay for Earth
  Observation
Living in the Physics and Machine Learning Interplay for Earth Observation
Gustau Camps-Valls
D. Svendsen
Jordi Cortés-Andrés
Álvaro Moreno-Martínez
Adrián Pérez-Suay
J. Adsuara
I. Martín
M. Piles
Jordi Munoz-Marí
Luca Martino
PINNAI4CE
41
6
0
18 Oct 2020
Deep FPF: Gain function approximation in high-dimensional setting
Deep FPF: Gain function approximation in high-dimensional setting
S. Y. Olmez
Amirhossein Taghvaei
P. Mehta
77
9
0
02 Oct 2020
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the
  Poisson-Nernst-Planck system via the Deep Neural Network Approach
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach
Jae Yong Lee
Jin Woo Jang
H. Hwang
40
13
0
28 Sep 2020
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