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"

6 / 6 papers shown

Title
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators Shao-Ting Chiu Junyuan Hong Ulisses Braga-Neto BDL 33 0 0 11 Oct 2024
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 39 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 34 5 0 19 Jan 2024
Towards Size-Independent Generalization Bounds for Deep Operator Nets Pulkit Gopalani Sayar Karmakar Dibyakanti Kumar Anirbit Mukherjee AI4CE 24 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 265 2,309 0 18 Oct 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 152 221 0 29 Sep 2019