A physics-informed operator regression framework for extracting data-driven continuum models

25 September 2020

Papers citing "A physics-informed operator regression framework for extracting data-driven continuum models"

21 / 21 papers shown

Title
DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations Rishikesh Ranade M. A. Nabian Kaustubh Tangsali Alexey Kamenev O. Hennigh Ram Cherukuri S. Choudhry AI4CE 94 2 0 23 Jan 2025
Physics-informed deep learning for infectious disease forecasting Y. Qian Éric Marty Avranil Basu Avranil Basu Eamon B. O'Dea Xianqiao Wang Spencer Fox Pejman Rohani John M. Drake He Li PINN AI4CE 86 2 0 16 Jan 2025
Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields Daniel Badawi Eduardo Gildin OOD AI4CE 21 4 0 13 Jul 2024
Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators Jiangce Chen Wenzhuo Xu Martha Baldwin Björn Nijhuis T. Boogaard Noelia Grande Gutiérrez S. Narra Christopher McComb AI4CE 28 2 0 04 Jul 2023
Nonlocality and Nonlinearity Implies Universality in Operator Learning S. Lanthaler Zong-Yi Li Andrew M. Stuart 26 17 0 26 Apr 2023
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations Xiongye Xiao De-An Cao Ruochen Yang Gaurav Gupta Gengshuo Liu Chenzhong Yin R. Balan P. Bogdan 74 9 0 04 Mar 2023
An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations Jianqing Zhu Juncai He Qiumei Huang 35 4 0 02 Feb 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 50 91 0 13 Dec 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 32 18 0 27 Oct 2022
A composable machine-learning approach for steady-state simulations on high-resolution grids Rishikesh Ranade C. Hill Lalit Ghule Jay Pathak AI4CE 36 7 0 11 Oct 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 21 9 0 02 Aug 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 20 158 0 12 Feb 2022
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning Chengping Rao Pu Ren Yang Liu Hao Sun AI4CE 45 28 0 28 Jan 2022
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 62 386 0 06 Nov 2021
A composable autoencoder-based iterative algorithm for accelerating numerical simulations Rishikesh Ranade C. Hill Haiyang He Amir Maleki Norman Chang Jay Pathak AI4CE 43 5 0 07 Oct 2021
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 126 202 0 28 Sep 2021
Convergence Rates for Learning Linear Operators from Noisy Data Maarten V. de Hoop Nikola B. Kovachki Nicholas H. Nelsen Andrew M. Stuart 21 54 0 27 Aug 2021
Neural Operator: Learning Maps Between Function Spaces Nikola B. Kovachki Zong-Yi Li Burigede Liu Kamyar Azizzadenesheli K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 52 441 0 19 Aug 2021
Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer B. Keith U. Khristenko B. Wohlmuth 27 7 0 23 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
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 268 2,309 0 18 Oct 2020