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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
A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model Jinchao Feng Sui Tang 82 0 0 11 May 2025
A general physics-constrained method for the modelling of equation's closure terms with sparse data Tian Chen Shengping Liu Li Liu Heng Yong PINN AI4CE 78 0 0 30 Apr 2025
Interpretable Machine Learning in Physics: A Review Sebastian Johann Wetzel Seungwoong Ha Raban Iten Miriam Klopotek Ziming Liu AI4CE 160 2 0 30 Mar 2025
Uncertainty propagation in feed-forward neural network models Jeremy Diamzon Daniele Venturi 132 0 0 27 Mar 2025
Exploration of Hepatitis B Virus Infection Dynamics through Virology-Informed Neural Network: A Novel Artificial Intelligence Approach Bikram Das Rupchand Sutradhar D C Dalal 95 0 0 12 Mar 2025
No Forgetting Learning: Memory-free Continual Learning Mohammad Ali Vahedifar Qi Zhang 80 0 0 06 Mar 2025
Al-Khwarizmi: Discovering Physical Laws with Foundation Models Christopher E. Mower Haitham Bou-Ammar AI4CE 201 2 0 03 Feb 2025
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs Krzysztof Kacprzyk Mihaela van der Schaar 289 0 0 30 Jan 2025
The Finite Element Neural Network Method: One Dimensional Study Mohammed Abda Elsa Piollet Christopher Blake Frédérick P. Gosselin 121 0 0 21 Jan 2025
Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift Teddy Lazebnik 126 1 0 12 Dec 2024
Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods Colby Fronk Linda R. Petzold 131 2 0 02 Dec 2024
Estimating unknown parameters in differential equations with a reinforcement learning based PSO method Wenkui Sun Xiaoya Fan Lijuan Jia Tinyi Chu Shing-Tung Yau Rongling Wu Zhong Wang 30 0 0 13 Nov 2024
Learning Interpretable Network Dynamics via Universal Neural Symbolic Regression Jiao Hu Jiaxu Cui Bo Yang AI4CE 90 0 0 11 Nov 2024
GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws Dimitrios G. Patsatzis Mario di Bernardo L. Russo Constantinos Siettos AI4CE 50 2 0 29 Oct 2024
Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods Colby Fronk Linda R. Petzold 90 5 0 08 Oct 2024
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 57 1 0 20 Sep 2024
General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations Sen Wang Peizhi Zhao Qinglong Ma Tao Song PINN 62 3 0 27 Aug 2024
Data-driven identification of latent port-Hamiltonian systems J. Rettberg Jonas Kneifl Julius Herb Patrick Buchfink Jörg Fehr B. Haasdonk PINN 82 2 0 15 Aug 2024
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation Mahdi Nasiri Sahel Iqbal Simo Särkkä AI4CE 43 2 0 12 Aug 2024
Introducing Ínside' Out of Distribution Teddy Lazebnik 113 1 0 05 Jul 2024
Science-Informed Deep Learning (ScIDL) With Applications to Wireless Communications Atefeh Termehchi Ekram Hossain Isaac Woungang 68 0 0 29 Jun 2024
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications Oisín M. Morrison F. Pichi J. Hesthaven AI4CE 72 6 0 05 Jun 2024
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification Paolo Conti Jonas Kneifl Andrea Manzoni A. Frangi Jörg Fehr Steven L. Brunton J. Nathan Kutz 95 6 0 31 May 2024
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media Matthaios Chatzopoulos P. Koutsourelakis AI4CE 81 3 0 29 May 2024
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory I. Kavrakov Gledson Rodrigo Tondo Guido Morgenthal AI4CE 119 1 0 21 May 2024
Stabilizing Backpropagation Through Time to Learn Complex Physics Patrick Schnell Nils Thuerey 105 2 0 03 May 2024
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks I. Char Youngseog Chung J. Abbate E. Kolemen Jeff Schneider 73 6 0 18 Apr 2024
A data-driven approach to modeling brain activity using differential equations Kuratov Andrey 75 0 0 14 Apr 2024
PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction Siming Shan Pengkai Wang Song Chen Jiaxu Liu Chao Xu Shengze Cai AI4CE DiffM 80 1 0 12 Apr 2024
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes Ming Zhong Dehao Liu Raymundo Arroyave U. Braga-Neto AI4CE SSL 69 1 0 08 Apr 2024
Suppressing Modulation Instability with Reinforcement Learning Nikolay Kalmykov R. Zagidullin Oleg Y. Rogov Sergey Rykovanov Dmitry V. Dylov 64 1 0 05 Apr 2024
tLaSDI: Thermodynamics-informed latent space dynamics identification Jun Sur Richard Park Siu Wun Cheung Youngsoo Choi Yeonjong Shin AI4CE 65 6 0 09 Mar 2024
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage Xuyang Li H. Bolandi Mahdi Masmoudi Talal Salem N. Lajnef Vishnu Boddeti AI4CE 27 4 0 23 Feb 2024
Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together? Hans Riess M. Veveakis Michael M. Zavlanos 130 2 0 05 Feb 2024
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks Dat Phan-Trong Hung The Tran A. Shilton Sunil R. Gupta 81 0 0 05 Feb 2024
Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inference R. Niven Laurent Cordier Ali Mohammad-Djafari Markus Abel M. Quade 91 6 0 30 Jan 2024
Data-Driven Discovery of PDEs via the Adjoint Method Mohsen Sadr Tony Tohme Kamal Youcef-Toumi PINN 115 1 0 30 Jan 2024
Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network Jiayi Xie Hongfeng Li Jin Cheng Qingrui Cai Hanbo Tan Lingyun Zu Xiaobo Qu Hongbin Han 70 2 0 23 Jan 2024
A novel framework for generalization of deep hidden physics models Vijay Kag Birupaksha Pal AI4CE 40 0 0 09 Jan 2024
Physics-informed Deep Learning to Solve Three-dimensional Terzaghi Consolidation Equation: Forward and Inverse Problems Biao Yuan Ana Heitor He Wang Xiaohui Chen AI4CE PINN 75 2 0 08 Jan 2024
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks Assaf Shmuel Oren Glickman Teddy Lazebnik 90 9 0 10 Nov 2023
Data-Driven Model Selections of Second-Order Particle Dynamics via Integrating Gaussian Processes with Low-Dimensional Interacting Structures Jinchao Feng Charles Kulick Sui Tang 112 2 0 01 Nov 2023
An Introduction to the Calibration of Computer Models Richard D. Wilkinson Christopher W. Lanyon 52 0 0 13 Oct 2023
An operator preconditioning perspective on training in physics-informed machine learning Tim De Ryck Florent Bonnet Siddhartha Mishra Emmanuel de Bezenac AI4CE 130 19 0 09 Oct 2023
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes Gledson Rodrigo Tondo Sebastian Rau I. Kavrakov Guido Morgenthal 87 6 0 21 Sep 2023
A spectrum of physics-informed Gaussian processes for regression in engineering E. Cross T. Rogers D. J. Pitchforth S. Gibson Matthew R. Jones 56 9 0 19 Sep 2023
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains Md Nasim Yexiang Xue 29 0 0 13 Sep 2023
Bayesian polynomial neural networks and polynomial neural ordinary differential equations Colby Fronk Jaewoong Yun Prashant Singh Linda R. Petzold BDL 31 4 0 17 Aug 2023
Size Lowerbounds for Deep Operator Networks Anirbit Mukherjee Amartya Roy AI4CE 68 3 0 11 Aug 2023
Physics-informed Gaussian process model for Euler-Bernoulli beam elements Gledson Rodrigo Tondo Sebastian Rau I. Kavrakov Guido Morgenthal 37 5 0 05 Aug 2023