Parametric Complexity Bounds for Approximating PDEs with Neural Networks

Parametric Complexity Bounds for Approximating PDEs with Neural Networks

3 March 2021

Zachary Chase Lipton

Andrej Risteski

Papers citing "Parametric Complexity Bounds for Approximating PDEs with Neural Networks"

7 / 7 papers shown

Title
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models Song Mei 3DV AI4CE DiffM 41 11 0 29 Apr 2024
Statistical Spatially Inhomogeneous Diffusion Inference Yinuo Ren Yiping Lu Lexing Ying Grant M. Rotskoff 20 2 0 10 Dec 2023
Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective Tanya Marwah Zachary Chase Lipton Jianfeng Lu Andrej Risteski 49 10 0 21 Oct 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent Yiping Lu Jose H. Blanchet Lexing Ying 35 7 0 15 May 2022
Tutorial on amortized optimization Brandon Amos OffRL 75 43 0 01 Feb 2022
On the Representation of Solutions to Elliptic PDEs in Barron Spaces Ziang Chen Jianfeng Lu Yulong Lu 32 26 0 14 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 208 2,287 0 18 Oct 2020