Cheng Jing — accepted AI-conference papers

2026

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs

Cheng Jing, Uvini Balasuriya Mudiyanselage, Abhishek Verma, Kallol Bera, Shahid Rauf, Kookjin Lee

ICLR 2026poster

Parameterized and coupled partial differential equations (PDEs) are central to modeling phenomena in science and engineering, yet neural operator methods that address both aspects remain limited. We extend Fourier neural operators (FNOs) with minimal architectural modifications along two directions.…

Cited by 0 Source ↗Scholar ↗

2026

Meta-learning Structure-Preserving Dynamics

Cheng Jing, Uvini Mudiyanselage, Woojin Cho, Minju Jo, Anthony Gruber, Kookjin Lee

ICML 2026poster

Structure-preserving approaches to dynamics discovery have demonstrated great potential for modeling physical systems due to their use of strong inductive biases, which enforce key features such as conservation laws and dissipative behavior. However, these models are typically trained on a per-confi…

Cited by 1 Source ↗Scholar ↗