ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2111.01356
  4. Cited By
DeepParticle: learning invariant measure by a deep neural network
  minimizing Wasserstein distance on data generated from an interacting
  particle method
v1v2v3 (latest)

DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method

2 November 2021
Zhongjian Wang
Jack Xin
Zhiwen Zhang
ArXiv (abs)PDFHTML

Papers citing "DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method"

26 / 26 papers shown
Title
Least-Squares ReLU Neural Network (LSNN) Method For Linear
  Advection-Reaction Equation
Least-Squares ReLU Neural Network (LSNN) Method For Linear Advection-Reaction Equation
Z. Cai
Jingshuang Chen
Min Liu
PINN
66
47
0
25 May 2021
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
500
2,444
0
18 Oct 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and
  Inverse PDE Problems with Noisy Data
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
Liu Yang
Xuhui Meng
George Karniadakis
PINN
233
786
0
13 Mar 2020
Alternating the Population and Control Neural Networks to Solve
  High-Dimensional Stochastic Mean-Field Games
Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games
A. Lin
Samy Wu Fung
Wuchen Li
L. Nurbekyan
Stanley J. Osher
77
74
0
24 Feb 2020
Deep regularization and direct training of the inner layers of Neural
  Networks with Kernel Flows
Deep regularization and direct training of the inner layers of Neural Networks with Kernel Flows
G. Yoo
H. Owhadi
61
21
0
19 Feb 2020
Deep Network Approximation for Smooth Functions
Deep Network Approximation for Smooth Functions
Jianfeng Lu
Zuowei Shen
Haizhao Yang
Shijun Zhang
114
247
0
09 Jan 2020
A Machine Learning Framework for Solving High-Dimensional Mean Field
  Game and Mean Field Control Problems
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems
Lars Ruthotto
Stanley Osher
Wuchen Li
L. Nurbekyan
Samy Wu Fung
AI4CE
118
220
0
04 Dec 2019
Deep least-squares methods: an unsupervised learning-based numerical
  method for solving elliptic PDEs
Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs
Z. Cai
Jingshuang Chen
Min Liu
Xinyu Liu
52
89
0
05 Nov 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal
  Space
Data-Driven Deep Learning of Partial Differential Equations in Modal Space
Kailiang Wu
D. Xiu
119
152
0
15 Oct 2019
Convergence Analysis of Machine Learning Algorithms for the Numerical
  Solution of Mean Field Control and Games: II -- The Finite Horizon Case
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: II -- The Finite Horizon Case
René Carmona
Mathieu Laurière
48
99
0
05 Aug 2019
Convergence Analysis of Machine Learning Algorithms for the Numerical
  Solution of Mean Field Control and Games: I -- The Ergodic Case
Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I -- The Ergodic Case
René Carmona
Mathieu Laurière
60
8
0
13 Jul 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
PINNAI4CE
108
868
0
18 Jan 2019
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep
  Network
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Zichao Long
Yiping Lu
Bin Dong
AI4CE
76
553
0
30 Nov 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks
Data Driven Governing Equations Approximation Using Deep Neural Networks
Tong Qin
Kailiang Wu
D. Xiu
PINN
74
273
0
13 Nov 2018
Deep Multiscale Model Learning
Deep Multiscale Model Learning
Yating Wang
Siu Wun Cheung
Eric T. Chung
Y. Efendiev
Min Wang
AI4CE
65
82
0
13 Jun 2018
Computational Optimal Transport
Computational Optimal Transport
Gabriel Peyré
Marco Cuturi
OT
224
2,149
0
01 Mar 2018
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate
  Modeling and Uncertainty Quantification
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
Yinhao Zhu
N. Zabaras
UQCVBDL
111
645
0
21 Jan 2018
Multistep Neural Networks for Data-driven Discovery of Nonlinear
  Dynamical Systems
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
M. Raissi
P. Perdikaris
George Karniadakis
PINN
149
266
0
04 Jan 2018
PDE-Net: Learning PDEs from Data
PDE-Net: Learning PDEs from Data
Zichao Long
Yiping Lu
Xianzhong Ma
Bin Dong
DiffMAI4CE
46
758
0
26 Oct 2017
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
123
1,387
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
91
2,066
0
24 Aug 2017
Deep learning-based numerical methods for high-dimensional parabolic
  partial differential equations and backward stochastic differential equations
Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E
Jiequn Han
Arnulf Jentzen
125
798
0
15 Jun 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
728
132,199
0
12 Jun 2017
Error bounds for approximations with deep ReLU networks
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
195
1,230
0
03 Oct 2016
On the Expressive Power of Deep Learning: A Tensor Analysis
On the Expressive Power of Deep Learning: A Tensor Analysis
Nadav Cohen
Or Sharir
Amnon Shashua
81
471
0
16 Sep 2015
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation
  Distances
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Marco Cuturi
OT
215
4,277
0
04 Jun 2013
1