Learning in latent spaces improves the predictive accuracy of deep neural operators

15 April 2023

Papers citing "Learning in latent spaces improves the predictive accuracy of deep neural operators"

5 / 5 papers shown

Title
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains Yusuke Yamazaki Ali Harandi Mayu Muramatsu A. Viardin Markus Apel T. Brepols Stefanie Reese Shahed Rezaei AI4CE 26 12 0 21 May 2024
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning Hao Liu Biraj Dahal Rongjie Lai Wenjing Liao AI4CE 21 5 0 19 Jan 2024
Towards Size-Independent Generalization Bounds for Deep Operator Nets Pulkit Gopalani Sayar Karmakar Dibyakanti Kumar Anirbit Mukherjee AI4CE 21 5 0 23 May 2022
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 2,281 0 18 Oct 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 139 219 0 29 Sep 2019