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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

28 November 2017
M. Raissi
P. Perdikaris
George Karniadakis
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations"

13 / 13 papers shown
Title
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
Eric Hedlin
Munawar Hayat
Fatih Porikli
Kwang Moo Yi
Shweta Mahajan
3DH
152
0
0
22 Dec 2024
MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
Yicun Huang
Changfu Zou
Yongqian Li
T. Wik
PINN
103
10
0
27 Apr 2023
Highly-scalable, physics-informed GANs for learning solutions of
  stochastic PDEs
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang
Sean Treichler
Thorsten Kurth
Keno Fischer
D. Barajas-Solano
...
Valentin Churavy
A. Tartakovsky
Michael Houston
P. Prabhat
George Karniadakis
AI4CE
82
39
0
29 Oct 2019
Hidden Physics Models: Machine Learning of Nonlinear Partial
  Differential Equations
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
M. Raissi
George Karniadakis
AI4CEPINN
87
1,144
0
02 Aug 2017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial
  Differential Equations
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations
M. Raissi
P. Perdikaris
George Karniadakis
101
268
0
29 Mar 2017
Opening the Black Box of Deep Neural Networks via Information
Opening the Black Box of Deep Neural Networks via Information
Ravid Shwartz-Ziv
Naftali Tishby
AI4CE
118
1,420
0
02 Mar 2017
Machine Learning of Linear Differential Equations using Gaussian
  Processes
Machine Learning of Linear Differential Equations using Gaussian Processes
M. Raissi
George Karniadakis
83
553
0
10 Jan 2017
Inferring solutions of differential equations using noisy multi-fidelity
  data
Inferring solutions of differential equations using noisy multi-fidelity data
M. Raissi
P. Perdikaris
George Karniadakis
AI4CE
67
290
0
16 Jul 2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed
  Systems
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi
Ashish Agarwal
P. Barham
E. Brevdo
Zhiwen Chen
...
Pete Warden
Martin Wattenberg
Martin Wicke
Yuan Yu
Xiaoqiang Zheng
294
11,155
0
14 Mar 2016
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINNAI4CEODL
181
2,824
0
20 Feb 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,433
0
22 Dec 2014
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
265
1,202
0
30 Nov 2014
Brittleness of Bayesian Inference Under Finite Information in a
  Continuous World
Brittleness of Bayesian Inference Under Finite Information in a Continuous World
H. Owhadi
C. Scovel
T. Sullivan
124
58
0
24 Apr 2013
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