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"

50 / 136 papers shown

Title
A Review of Physics-based Machine Learning in Civil Engineering S. Vadyala S. N. Betgeri J. Matthews Elizabeth Matthews AI4CE 25 152 0 09 Oct 2021
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 34 68 0 30 Sep 2021
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks Jian Cheng Wong C. Ooi Abhishek Gupta Yew-Soon Ong AI4CE PINN SSL 18 77 0 20 Sep 2021
Physics-constrained Deep Learning for Robust Inverse ECG Modeling Jianxin Xie B. Yao 30 21 0 26 Jul 2021
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs Pu Ren Chengping Rao Yang Liu Jianxun Wang Hao Sun DiffM AI4CE 41 193 0 26 Jun 2021
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space Zhen Chen V. Churchill Kailiang Wu D. Xiu AI4CE 14 47 0 07 Jun 2021
Learning particle swarming models from data with Gaussian processes Jinchao Feng Charles Kulick Yunxiang Ren Sui Tang 26 5 0 04 Jun 2021
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks Friedrich Bethke R. Pausch Patrick Stiller A. Debus Michael Bussmann Nico Hoffmann 27 2 0 01 Jun 2021
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs Jérome Darbon P. Dower Tingwei Meng 8 22 0 07 May 2021
Direct Prediction of Steady-State Flow Fields in Meshed Domain with Graph Networks Lukas Harsch S. Riedelbauch AI4CE 59 12 0 06 May 2021
Neural graphical modelling in continuous-time: consistency guarantees and algorithms Alexis Bellot K. Branson M. Schaar CML AI4TS 24 43 0 06 May 2021
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks Jian Cheng Wong C. Ooi P. Chiu M. Dao PINN AI4CE 27 4 0 05 May 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 71 223 0 26 Apr 2021
Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems? A. Afonin Michael Chertkov 17 3 0 07 Apr 2021
A hybrid inference system for improved curvature estimation in the level-set method using machine learning Luis Ángel Larios-Cárdenas Frédéric Gibou 21 6 0 07 Apr 2021
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation Q. Du Yiqi Gu Haizhao Yang Chao Zhou 26 20 0 21 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Transferable Model for Shape Optimization subject to Physical Constraints Lukas Harsch Johannes Burgbacher S. Riedelbauch AI4CE 24 1 0 19 Mar 2021
Multi-objective discovery of PDE systems using evolutionary approach M. Maslyaev A. Hvatov 23 5 0 11 Mar 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 30 54 0 25 Feb 2021
Learning Contact Dynamics using Physically Structured Neural Networks Andreas Hochlehnert Alexander Terenin Steindór Sæmundsson M. Deisenroth 19 16 0 22 Feb 2021
Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems Laurent Pagnier Michael Chertkov 42 49 0 12 Feb 2021
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate Hongwei Guo X. Zhuang Timon Rabczuk AI4CE 19 433 0 04 Feb 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition S. Fresca Andrea Manzoni AI4CE 21 212 0 28 Jan 2021
Can Transfer Neuroevolution Tractably Solve Your Differential Equations? Jian Cheng Wong Abhishek Gupta Yew-Soon Ong 28 21 0 06 Jan 2021
Hybrid FEM-NN models: Combining artificial neural networks with the finite element method Sebastian K. Mitusch S. Funke M. Kuchta AI4CE 31 93 0 04 Jan 2021
A Physics-Informed Deep Learning Paradigm for Car-Following Models Zhaobin Mo Xuan Di Rongye Shi PINN AI4CE 27 131 0 24 Dec 2020
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
A Data Driven Method for Computing Quasipotentials Bo Lin Qianxiao Li W. Ren 16 13 0 13 Dec 2020
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data Priyabrata Saha Saibal Mukhopadhyay AI4CE 21 4 0 30 Nov 2020
Data-driven Identification of 2D Partial Differential Equations using extracted physical features Kazem Meidani A. Farimani 21 17 0 20 Oct 2020
Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems Jason D Miller Sui Tang Ming Zhong Mauro Maggioni 24 18 0 08 Oct 2020
Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning Hongwei Guo X. Zhuang Timon Rabczuk 20 82 0 03 Oct 2020
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications N. Zobeiry K. D. Humfeld AI4CE 15 265 0 28 Sep 2020
Discovery of Governing Equations with Recursive Deep Neural Networks Jia Zhao Jarrod Mau PINN 32 6 0 24 Sep 2020
Bridging the Gap: Machine Learning to Resolve Improperly Modeled Dynamics Maan Qraitem D. Kularatne Eric Forgoston M. A. Hsieh AI4CE 28 10 0 23 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 33 878 0 28 Jul 2020
Learning Variational Data Assimilation Models and Solvers Ronan Fablet Bertrand Chapron Lucas Drumetz É. Mémin O. Pannekoucke F. Rousseau 6 67 0 25 Jul 2020
Expressivity of Deep Neural Networks Ingo Gühring Mones Raslan Gitta Kutyniok 16 51 0 09 Jul 2020
Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann Julien N. P. Martel Alexander W. Bergman David B. Lindell Gordon Wetzstein AI4TS 47 2,486 0 17 Jun 2020
A generative adversarial network approach to (ensemble) weather prediction Alexander Bihlo AI4Cl 23 77 0 13 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 17 222 0 10 Jun 2020
Space-time deep neural network approximations for high-dimensional partial differential equations F. Hornung Arnulf Jentzen Diyora Salimova AI4CE 29 19 0 03 Jun 2020
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows Derek Onken Lars Ruthotto BDL 32 51 0 27 May 2020
Sparse Methods for Automatic Relevance Determination S. Rudy T. Sapsis 12 16 0 18 May 2020
Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data Hao Xu Dongxiao Zhang Junsheng Zeng 25 57 0 16 May 2020
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks E. Haghighat R. Juanes AI4CE PINN 17 21 0 11 May 2020
Learning reduced systems via deep neural networks with memory Xiaohang Fu L. Chang D. Xiu 11 32 0 20 Mar 2020
Learning to Fly via Deep Model-Based Reinforcement Learning Philip Becker-Ehmck Maximilian Karl Jan Peters Patrick van der Smagt SSL 35 37 0 19 Mar 2020
Methods to Recover Unknown Processes in Partial Differential Equations Using Data Zhen Chen Kailiang Wu D. Xiu 22 3 0 05 Mar 2020