Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

20 January 2018

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

36 / 136 papers shown

Title
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty Nicholas Galioto Alex Gorodetsky 17 32 0 04 Mar 2020
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction Vincent Le Guen Nicolas Thome AI4CE PINN 91 289 0 03 Mar 2020
Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations T. Kadeethum T. Jørgensen H. Nick PINN AI4CE 17 106 0 19 Feb 2020
PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations O. Pannekoucke Ronan Fablet AI4CE PINN DiffM 16 8 0 03 Feb 2020
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles X. Jia J. Willard Anuj Karpatne J. Read Jacob Aaron Zwart M. Steinbach Vipin Kumar AI4CE PINN 26 207 0 28 Jan 2020
On generalized residue network for deep learning of unknown dynamical systems Zhen Chen D. Xiu AI4CE 19 46 0 23 Jan 2020
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm Hao Xu Haibin Chang Dongxiao Zhang AI4CE 25 87 0 21 Jan 2020
A Derivative-Free Method for Solving Elliptic Partial Differential Equations with Deep Neural Networks Jihun Han Mihai Nica A. Stinchcombe 22 49 0 17 Jan 2020
Understanding and mitigating gradient pathologies in physics-informed neural networks Sizhuang He Yujun Teng P. Perdikaris AI4CE PINN 35 291 0 13 Jan 2020
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs S. Fresca Luca Dede' Andrea Manzoni AI4CE 17 258 0 12 Jan 2020
Discovery of Dynamics Using Linear Multistep Methods Rachael Keller Q. Du 31 36 0 29 Dec 2019
Uniform error estimates for artificial neural network approximations for heat equations Lukas Gonon Philipp Grohs Arnulf Jentzen David Kofler David Siska 29 34 0 20 Nov 2019
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe H. Qin 32 30 0 22 Oct 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 11 149 0 15 Oct 2019
Data-driven discovery of free-form governing differential equations Steven Atkinson W. Subber Liping Wang Genghis Khan Philippe Hawi R. Ghanem 14 43 0 27 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 17 9 0 15 Sep 2019
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 159 1,342 0 27 Aug 2019
Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development Kun Qian Abduallah A. Mohamed Christian G. Claudel AI4CE 16 25 0 23 Aug 2019
Neural Dynamics on Complex Networks Chengxi Zang Fei Wang AI4CE 35 68 0 18 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 14 69 0 13 Aug 2019
Space-time error estimates for deep neural network approximations for differential equations Philipp Grohs F. Hornung Arnulf Jentzen Philipp Zimmermann 29 33 0 11 Aug 2019
Learning the Tangent Space of Dynamical Instabilities from Data Antoine Blanchard T. Sapsis 8 8 0 24 Jul 2019
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning Peter Y. Lu Samuel Kim Marin Soljacic AI4CE 22 59 0 13 Jul 2019
Deep splitting method for parabolic PDEs C. Beck S. Becker Patrick Cheridito Arnulf Jentzen Ariel Neufeld 23 125 0 08 Jul 2019
Structure-preserving Method for Reconstructing Unknown Hamiltonian Systems from Trajectory Data Kailiang Wu Tong Qin D. Xiu 13 31 0 24 May 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs Gitta Kutyniok P. Petersen Mones Raslan R. Schneider 20 197 0 31 Mar 2019
Learning Dynamical Systems from Partial Observations Ibrahim Ayed Emmanuel de Bézenac Arthur Pajot J. Brajard Patrick Gallinari AI4TS 30 89 0 26 Feb 2019
Physics-informed deep generative models Yibo Yang P. Perdikaris AI4CE PINN 21 57 0 09 Dec 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 32 270 0 13 Nov 2018
The Newton Scheme for Deep Learning Junqing Qiu Guoren Zhong Yihua Lu Kun Xin Huihuan Qian Xi Zhu PINN 25 3 0 16 Oct 2018
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis M. A. Nabian Hadi Meidani PINN AI4CE 21 57 0 11 Oct 2018
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data M. Raissi A. Yazdani George Karniadakis AI4CE PINN 19 158 0 13 Aug 2018
Machine Learning of Space-Fractional Differential Equations Mamikon A. Gulian M. Raissi P. Perdikaris George Karniadakis 27 46 0 02 Aug 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 101 4,940 0 19 Jun 2018
Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations M. Raissi 20 183 0 19 Apr 2018
Convolutional Neural Networks combined with Runge-Kutta Methods Mai Zhu Bo Chang Chong Fu AI4CE 41 52 0 24 Feb 2018