Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport

6 December 2019

Papers citing "Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport"

28 / 28 papers shown

Title
Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method Conor Rowan K. Maute Alireza Doostan AI4CE 58 0 0 08 May 2025
Separable Physics-Informed Neural Networks for the solution of elasticity problems V. A. Es'kin Danil V. Davydov Julia V. Guréva Alexey O. Malkhanov Mikhail E. Smorkalov PINN AI4CE 43 2 0 24 Jan 2024
Computationally Efficient and Error Aware Surrogate Construction for Numerical Solutions of Subsurface Flow Through Porous Media Aleksei G. Sorokin Aleksandra Pachalieva Daniel O’Malley James M. Hyman F. J. Hickernell N. W. Hengartner 29 1 0 20 Oct 2023
Bayesian Physics-Informed Neural Network for the Forward and Inverse Simulation of Engineered Nano-particles Mobility in a Contaminated Aquifer Shikhar Nilabh F. Grandia 14 0 0 14 Aug 2023
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks Hyeonjung Jung Jung Jayant Gupta B. Jayaprakash Matthew J. Eagon Harish Selvam Carl Molnar W. Northrop Shashi Shekhar AI4CE 56 5 0 26 Apr 2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences Chaopeng Shen A. Appling Pierre Gentine Toshiyuki Bandai H. Gupta ... Chris Rackauckas Tirthankar Roy Chonggang Xu Binayak Mohanty K. Lawson AI4CE 60 14 0 10 Jan 2023
Physics-informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media Salah A. Faroughi Ramin Soltanmohammad Pingki Datta S. K. Mahjour S. Faroughi 34 23 0 17 Dec 2022
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires J. Dabrowski D. Pagendam J. Hilton Conrad Sanderson Dan MacKinlay C. Huston Andrew Bolt Petra Kuhnert PINN 52 17 0 02 Dec 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 37 18 0 27 Oct 2022
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain Pu Ren Chengping Rao Su Chen Jian-Xun Wang Hao Sun Yang Liu 61 42 0 25 Oct 2022
Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer Shikhar Nilabh F. Grandia 11 3 0 25 Oct 2022
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 29 12 0 19 Oct 2022
A Deep Learning Approach for the solution of Probability Density Evolution of Stochastic Systems S. Pourtakdoust Amir H. Khodabakhsh 40 12 0 05 Jul 2022
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method Shahed Rezaei Ali Harandi Ahmad Moeineddin Bai-Xiang Xu Stefanie Reese 28 114 0 27 Jun 2022
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) J. Abbasi Paal Ostebo Andersen PINN AI4CE 35 15 0 29 May 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 34 14 0 06 May 2022
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models Su Jiang L. Durlofsky AI4CE 27 29 0 23 Apr 2022
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process Thi Nguyen Khoa Nguyen T. Dairay Raphael Meunier Mathilde Mougeot PINN AI4CE 90 30 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 38 1,205 0 14 Jan 2022
Physics-constrained deep neural network method for estimating parameters in a redox flow battery Qizhi He P. Stinis A. Tartakovsky 30 33 0 21 Jun 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 53 39 0 03 May 2021
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems Samuel J. Raymond David B. Camarillo PINN AI4CE 52 12 0 30 Apr 2021
CCSNet: a deep learning modeling suite for CO $_2$ storage Gege Wen C. Hay S. Benson 40 73 0 05 Apr 2021
Meshless physics-informed deep learning method for three-dimensional solid mechanics Diab W. Abueidda Q. Lu S. Koric AI4CE 41 114 0 02 Dec 2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture S. Niaki E. Haghighat Trevor Campbell Xinglong Li R. Vaziri AI4CE 23 205 0 27 Nov 2020
Deep-Learning based Inverse Modeling Approaches: A Subsurface Flow Example Nanzhe Wang Haibin Chang Dongxiao Zhang AI4CE 32 59 0 28 Jul 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao Sun Yang Liu PINN AI4CE 35 222 0 10 Jun 2020
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 52 1,498 0 10 Jul 2019