Physics-Informed Deep Neural Operator Networks

Physics-Informed Deep Neural Operator Networks

8 July 2022

George Karniadakis

Papers citing "Physics-Informed Deep Neural Operator Networks"

16 / 16 papers shown

Title
Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition Wei Wang Maryam Hakimzadeh Haihui Ruan Somdatta Goswami AI4CE 27 0 0 15 Apr 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 39 0 0 02 Mar 2025
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 56 0 0 21 Feb 2025
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 52 0 0 10 Feb 2025
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 64 10 0 28 Jan 2025
Solving Sparse Finite Element Problems on Neuromorphic Hardware Bradley H. Theilman J. Aimone 34 0 0 17 Jan 2025
Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems Nicholas Karris Evangelos A. Nikitopoulos Ioannis G. Kevrekidis Seungjoon Lee Alexander Cloninger OT 30 0 0 10 Jan 2025
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems Sumanth Kumar Boya Deepak Subramani AI4CE 94 0 0 12 Dec 2024
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators Chuwei Wang Julius Berner Zongyi Li Di Zhou Jiayun Wang Jane Bae Anima Anandkumar AI4CE 28 1 0 09 Aug 2024
PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model M. Hassanaly Peter J. Weddle Ryan N. King Subhayan De Alireza Doostan Corey R. Randall Eric J. Dufek Andrew M. Colclasure Kandler Smith 14 6 0 28 Dec 2023
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 59 58 0 23 May 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 59 39 0 16 May 2022
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 124 198 0 28 Sep 2021
Arbitrary-Depth Universal Approximation Theorems for Operator Neural Networks Annan Yu Chloe Becquey Diana Halikias Matthew Esmaili Mallory Alex Townsend 57 8 0 23 Sep 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 203 2,272 0 18 Oct 2020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 145 1,333 0 27 Aug 2019