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 160 3 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 129 2 0 16 Jan 2025
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 126 9 0 04 Mar 2023
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 520 42,449 0 03 Dec 2019
Data-Driven Deep Learning of Partial Differential Equations in Modal Space Kailiang Wu D. Xiu 119 152 0 15 Oct 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 248 2,131 0 08 Oct 2019
GMLS-Nets: A framework for learning from unstructured data Nathaniel Trask Ravi G. Patel B. Gross P. Atzberger 32 42 0 07 Sep 2019
PiNet: A Permutation Invariant Graph Neural Network for Graph Classification Peter Meltzer Marcelo Daniel Gutierrez Mallea Peter J Bentley GNN 55 12 0 08 May 2019
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks Dongkun Zhang Ling Guo George Karniadakis AI4CE 71 213 0 03 May 2019
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network Zichao Long Yiping Lu Bin Dong AI4CE 76 552 0 30 Nov 2018
Data Driven Governing Equations Approximation Using Deep Neural Networks Tong Qin Kailiang Wu D. Xiu PINN 74 273 0 13 Nov 2018
Nonlinear integro-differential operator regression with neural networks Ravi G. Patel O. Desjardins 45 18 0 19 Oct 2018
Numerical Aspects for Approximating Governing Equations Using Data Kailiang Wu D. Xiu 49 68 0 24 Sep 2018
Relational inductive biases, deep learning, and graph networks Peter W. Battaglia Jessica B. Hamrick V. Bapst Alvaro Sanchez-Gonzalez V. Zambaldi ... Pushmeet Kohli M. Botvinick Oriol Vinyals Yujia Li Razvan Pascanu AI4CE NAI 761 3,121 0 04 Jun 2018
CubeNet: Equivariance to 3D Rotation and Translation Daniel E. Worrall Gabriel J. Brostow 3DPC 77 143 0 12 Apr 2018
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds Nathaniel Thomas Tess E. Smidt S. Kearnes Lusann Yang Li Li Kai Kohlhoff Patrick F. Riley 3DPC 95 973 0 22 Feb 2018
Data Augmentation by Pairing Samples for Images Classification H. Inoue 160 422 0 09 Jan 2018
The Effectiveness of Data Augmentation in Image Classification using Deep Learning Luis Perez Jason Wang 80 2,789 0 13 Dec 2017
Learning SO(3) Equivariant Representations with Spherical CNNs Carlos Esteves Christine Allen-Blanchette A. Makadia Kostas Daniilidis 107 515 0 17 Nov 2017
Understanding data augmentation for classification: when to warp? S. Wong Adam Gatt V. Stamatescu Mark D Mcdonnell 80 905 0 28 Sep 2016
Rotation-invariant convolutional neural networks for galaxy morphology prediction Sander Dieleman K. Willett J. Dambre 98 653 0 24 Mar 2015