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Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

2 August 2017

Papers citing "Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations"

50 / 318 papers shown

Title
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 84 39 0 29 Oct 2019
FD-Net with Auxiliary Time Steps: Fast Prediction of PDEs using Hessian-Free Trust-Region Methods Nur Sila Gulgec Zheng Shi Neil Deshmukh S. Pakzad Martin Takáč 53 6 0 28 Oct 2019
Coercing Machine Learning to Output Physically Accurate Results Z. Geng Dan Johnson Ronald Fedkiw 3DH 46 37 0 21 Oct 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 127 153 0 15 Oct 2019
Solving Optical Tomography with Deep Learning Yuwei Fan Lexing Ying 59 17 0 10 Oct 2019
Data-driven discovery of free-form governing differential equations Steven Atkinson W. Subber Liping Wang Genghis Khan Philippe Hawi R. Ghanem 58 43 0 27 Sep 2019
A Multi-level procedure for enhancing accuracy of machine learning algorithms K. Lye Siddhartha Mishra Roberto Molinaro 66 32 0 20 Sep 2019
Machine Discovery of Partial Differential Equations from Spatiotemporal Data Ye Yuan Junlin Li Liang Li Frank Jiang Xiuchuan Tang ... J. Gonçalves H. Voss Xiuting Li J. Kurths Han Ding AI4CE 53 9 0 15 Sep 2019
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints M. Lutter Boris Belousov Kim D. Listmann Debora Clever Jan Peters 53 22 0 13 Sep 2019
Coarse-scale PDEs from fine-scale observations via machine learning Seungjoon Lee M. Kooshkbaghi K. Spiliotis Constantinos Siettos Ioannis G. Kevrekidis DiffM AI4CE 50 83 0 12 Sep 2019
GMLS-Nets: A framework for learning from unstructured data Nathaniel Trask Ravi G. Patel B. Gross P. Atzberger 59 42 0 07 Sep 2019
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing M. Mudunuru S. Karra 45 11 0 28 Aug 2019
DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data Hao Xu Haibin Chang Dongxiao Zhang AI4CE 61 70 0 13 Aug 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 69 33 0 11 Aug 2019
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data Yifan Sun Linan Zhang Hayden Schaeffer AI4TS 72 92 0 08 Aug 2019
Classification of chaotic time series with deep learning Nicolas Boullé V. Dallas Y. Nakatsukasa D. Samaddar AI4TS 29 56 0 26 Jul 2019
Learning the Tangent Space of Dynamical Instabilities from Data Antoine Blanchard T. Sapsis 134 8 0 24 Jul 2019
Stability selection enables robust learning of partial differential equations from limited noisy data Suryanarayana Maddu B. Cheeseman I. Sbalzarini Christian L. Müller 58 19 0 17 Jul 2019
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning M. Lutter Christian Ritter Jan Peters PINN AI4CE 65 381 0 10 Jul 2019
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data Xihaier Luo A. Kareem BDL UQCV 42 28 0 08 Jul 2019
Physics Informed Extreme Learning Machine (PIELM) -- A rapid method for the numerical solution of partial differential equations Vikas Dwivedi Balaji Srinivasan PINN 79 194 0 08 Jul 2019
SNODE: Spectral Discretization of Neural ODEs for System Identification A. Quaglino Marco Gallieri Jonathan Masci Jan Koutník AI4TS 106 48 0 17 Jun 2019
Tackling Climate Change with Machine Learning David Rolnick P. Donti L. Kaack K. Kochanski Alexandre Lacoste ... Demis Hassabis John C. Platt F. Creutzig J. Chayes Yoshua Bengio AI4Cl AI4CE 108 813 0 10 Jun 2019
Solving Electrical Impedance Tomography with Deep Learning Yuwei Fan Lexing Ying 99 104 0 06 Jun 2019
Machine Learning for Fluid Mechanics Steven Brunton B. R. Noack Petros Koumoutsakos AI4CE PINN 91 2,136 0 27 May 2019
Neural Jump Stochastic Differential Equations Junteng Jia Austin R. Benson BDL 81 229 0 24 May 2019
Explainable Machine Learning for Scientific Insights and Discoveries R. Roscher B. Bohn Marco F. Duarte Jochen Garcke XAI 118 675 0 21 May 2019
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks Dongkun Zhang Ling Guo George Karniadakis AI4CE 83 214 0 03 May 2019
On the approximation of the solution of partial differential equations by artificial neural networks trained by a multilevel Levenberg-Marquardt method H. Calandra Serge Gratton E. Riccietti X. Vasseur 28 7 0 09 Apr 2019
Deep learning observables in computational fluid dynamics K. Lye Siddhartha Mishra Deep Ray OOD AI4CE 120 159 0 07 Mar 2019
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors N. Benjamin Erichson L. Mathelin Z. Yao Steven L. Brunton Michael W. Mahoney J. Nathan Kutz AI4CE 61 34 0 20 Feb 2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime Constantin Grigo P. Koutsourelakis AI4CE 129 26 0 11 Feb 2019
Differentiable Physics-informed Graph Networks Sungyong Seo Yan Liu PINN AI4CE 94 67 0 08 Feb 2019
Active Image Synthesis for Efficient Labeling Jialei Chen Yujia Xie Kan Wang Chuck Zhang M. Vannan Ben Wang Z. Qian MedIm 72 11 0 05 Feb 2019
Fast Neural Network Predictions from Constrained Aerodynamics Datasets Cristina White D. Ushizima C. Farhat AI4CE 55 18 0 26 Jan 2019
Pricing options and computing implied volatilities using neural networks Shuaiqiang Liu C. Oosterlee S. Bohté 75 123 0 25 Jan 2019
Fleet Prognosis with Physics-informed Recurrent Neural Networks R. Nascimento F. Viana 27 52 0 16 Jan 2019
Physics-informed deep generative models Yibo Yang P. Perdikaris AI4CE PINN 87 59 0 09 Dec 2018
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network Zichao Long Yiping Lu Bin Dong AI4CE 89 553 0 30 Nov 2018
Networks for Nonlinear Diffusion Problems in Imaging Simon Arridge A. Hauptmann DiffM MedIm 70 18 0 29 Nov 2018
Recovery guarantees for polynomial approximation from dependent data with outliers L. Ho Hayden Schaeffer Giang Tran Rachel A. Ward 53 2 0 25 Nov 2018
An overview of deep learning in medical imaging focusing on MRI A. Lundervold A. Lundervold OOD 112 1,649 0 25 Nov 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 92 275 0 13 Nov 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 145 361 0 09 Nov 2018
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 134 367 0 05 Nov 2018
Identification of physical processes via combined data-driven and data-assimilation methods Haibin Chang Dongxiao Zhang 67 32 0 29 Oct 2018
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting Philipp Haehnel Jakub Mareˇcek Julien Monteil Fearghal O'Donncha AI4CE 29 39 0 22 Oct 2018
BCR-Net: a neural network based on the nonstandard wavelet form Yuwei Fan Cindy Orozco Bohorquez Lexing Ying 71 47 0 20 Oct 2018
Nonlinear integro-differential operator regression with neural networks Ravi G. Patel O. Desjardins 64 18 0 19 Oct 2018
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis M. A. Nabian Hadi Meidani PINN AI4CE 68 58 0 11 Oct 2018