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A comparative study of physics-informed neural network models for
  learning unknown dynamics and constitutive relations

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

2 April 2019
R. Tipireddy
P. Perdikaris
P. Stinis
A. Tartakovsky
    PINN
ArXiv (abs)PDFHTML

Papers citing "A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations"

6 / 6 papers shown
Title
Physics-informed deep generative models
Physics-informed deep generative models
Yibo Yang
P. Perdikaris
AI4CEPINN
79
59
0
09 Dec 2018
Neural Ordinary Differential Equations
Neural Ordinary Differential Equations
T. Chen
Yulia Rubanova
J. Bettencourt
David Duvenaud
AI4CE
452
5,168
0
19 Jun 2018
Physics-constrained, data-driven discovery of coarse-grained dynamics
Physics-constrained, data-driven discovery of coarse-grained dynamics
L. Felsberger
P. Koutsourelakis
AI4CE
50
19
0
11 Feb 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
152
266
0
04 Jan 2018
Physics Informed Deep Learning (Part II): Data-driven Discovery of
  Nonlinear Partial Differential Equations
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
M. Raissi
P. Perdikaris
George Karniadakis
PINNAI4CE
96
615
0
28 Nov 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
97
2,067
0
24 Aug 2017
1