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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
Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach Dimitrios G. Patsatzis Lucia Russo Ioannis G. Kevrekidis Constantinos Siettos 52 15 0 12 Jul 2022
D-CIPHER: Discovery of Closed-form Partial Differential Equations Krzysztof Kacprzyk Zhaozhi Qian M. Schaar AI4CE 76 1 0 21 Jun 2022
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions Ryan Lopez P. Atzberger AI4CE 112 8 0 10 Jun 2022
Constraining Gaussian processes for physics-informed acoustic emission mapping Matthew R. Jones T. Rogers E. Cross AI4CE 82 16 0 03 Jun 2022
Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data Seungjoon Lee Y. M. Psarellis Constantinos Siettos Ioannis G. Kevrekidis AI4CE 60 28 0 26 May 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 121 62 0 23 May 2022
Neural ODEs with Irregular and Noisy Data P. Goyal P. Benner 56 4 0 19 May 2022
Data-aided Underwater Acoustic Ray Propagation Modeling Kexin Li M. Chitre 52 13 0 12 May 2022
RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations Shulan Qin Min Li Tao Xu Shaotong Dong 100 9 0 29 Apr 2022
Neural Implicit Representations for Physical Parameter Inference from a Single Video Florian Hofherr Lukas Koestler Florian Bernard Daniel Cremers AI4CE 114 10 0 29 Apr 2022
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks Raghav Gnanasambandam Bo Shen Jihoon Chung Xubo Yue Zhenyu Zhen Kong LRM 153 12 0 26 Apr 2022
U-NO: U-shaped Neural Operators Md Ashiqur Rahman Zachary E. Ross Kamyar Azizzadenesheli AI4CE 125 146 0 23 Apr 2022
Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data Mathies Wedler M. Stender M. Klein Svenja Ehlers N. Hoffmann 48 8 0 14 Apr 2022
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems Jiahao Zhang Shiqi Zhang Guang Lin 95 15 0 07 Apr 2022
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations Jiahao Zhang Shiqi Zhang Guang Lin 81 3 0 06 Apr 2022
Discovering Governing Equations by Machine Learning implemented with Invariance Chao Chen Xiaowei Jin Hui Li PINN AI4CE 26 1 0 29 Mar 2022
An Adaptive and Scalable ANN-based Model-Order-Reduction Method for Large-Scale TO Designs Renkai Tan Chaojun Qian Dan Xu Wenjing Ye AI4CE 32 2 0 20 Mar 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations Tim De Ryck Ameya Dilip Jagtap S. Mishra PINN 126 118 0 17 Mar 2022
Multigrid-augmented deep learning preconditioners for the Helmholtz equation Yael Azulay Eran Treister AI4CE 73 31 0 14 Mar 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 88 42 0 06 Mar 2022
Thermodynamics-informed graph neural networks Quercus Hernandez Alberto Badías Francisco Chinesta Elías Cueto AI4CE PINN 80 32 0 03 Mar 2022
Machine Learning based refinement strategies for polyhedral grids with applications to Virtual Element and polyhedral Discontinuous Galerkin methods Paola F. Anotnietti F. Dassi E. Manuzzi 47 16 0 25 Feb 2022
State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning Sourav Das S. Tesfamariam PINN AI4CE 79 20 0 13 Feb 2022
Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints Christian Soize 49 6 0 10 Feb 2022
Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach Evangelos Galaris Gianluca Fabiani I. Gallos Ioannis G. Kevrekidis Constantinos Siettos AI4CE 102 42 0 31 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 123 1,293 0 14 Jan 2022
Surrogate Model for Shallow Water Equations Solvers with Deep Learning Yalan Song Chaopeng Shen Xiaofeng Liu AI4CE 47 5 0 20 Dec 2021
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions Bibit Bianchini Mathew Halm Nikolai Matni Michael Posa 83 12 0 13 Dec 2021
Data-driven discoveries of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes Zijian Zhou Li Wang Weifang Weng Zhenya Yan 63 19 0 18 Nov 2021
Learning Free-Surface Flow with Physics-Informed Neural Networks Raphael Leiteritz Marcel Hurler Dirk Pflüger PINN AI4CE 53 7 0 17 Nov 2021
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network Rui Xu Dongxiao Zhang Nanzhe Wang AI4CE 85 17 0 14 Nov 2021
A Neural Network Ensemble Approach to System Identification Elisa Negrini G. Citti L. Capogna 36 2 0 15 Oct 2021
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks Sagi Shaier M. Raissi P. Seshaiyer PINN AI4CE 98 26 0 11 Oct 2021
A composable autoencoder-based iterative algorithm for accelerating numerical simulations Rishikesh Ranade C. Hill Haiyang He Amir Maleki Norman Chang Jay Pathak AI4CE 64 6 0 07 Oct 2021
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs Biswajit Khara Aditya Balu Ameya Joshi Soumik Sarkar Chinmay Hegde A. Krishnamurthy Baskar Ganapathysubramanian 104 20 0 04 Oct 2021
Differentiable Spline Approximations Minsu Cho Aditya Balu Ameya Joshi Anjana Prasad Biswajit Khara Soumik Sarkar Baskar Ganapathysubramanian A. Krishnamurthy Chinmay Hegde 58 4 0 04 Oct 2021
Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint P. Goyal P. Benner 81 9 0 23 Sep 2021
A Latent Restoring Force Approach to Nonlinear System Identification T. Rogers Tobias Friis 84 18 0 22 Sep 2021
Performance and accuracy assessments of an incompressible fluid solver coupled with a deep Convolutional Neural Network Ekhi Ajuria Illarramendi M. Bauerheim B. Cuenot 89 21 0 20 Sep 2021
Low-rank statistical finite elements for scalable model-data synthesis Connor Duffin E. Cripps T. Stemler Mark Girolami 71 11 0 10 Sep 2021
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models Ling Guo Hao Wu Tao Zhou AI4CE 77 48 0 30 Aug 2021
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data L. Ho Nicholas Richardson Giang Tran 80 3 0 24 Aug 2021
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations Y. Kumar S. Chakraborty 64 8 0 24 Aug 2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Georg Gottwald Sebastian Reich AI4CE 94 38 0 08 Aug 2021
Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data Christophe Bonneville Christopher Earls 84 14 0 05 Aug 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization F. Lejarza M. Baldea AI4CE 79 39 0 30 Jul 2021
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations Zhiming Zhang Yongming Liu 41 11 0 08 Jul 2021
Generalization Error Analysis of Neural networks with Gradient Based Regularization Lingfeng Li X. Tai Jiang Yang 35 4 0 06 Jul 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 96 69 0 02 Jul 2021
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs Tim De Ryck Siddhartha Mishra PINN 99 103 0 28 Jun 2021