1 citations · 1 across the 3 of their papers we have counts for
4 papers
DyMixOp: A Neural Operator Designed from a Complex Dynamics Perspective with Local-Global Mixing for Solving PDEs
Pengyu Lai, Yixiao Chen, Dewu Yang +3
A primary challenge in using neural networks to approximate nonlinear dynamical systems governed by partial differential equations (PDEs) lies in recasting these systems into a tra…
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
Jing Wang, Biao Chen, Hairun Xie +4
Physics-informed neural operators have emerged as a powerful paradigm for solving parametric partial differential equations (PDEs), particularly in the aerospace field, enabling th…
Neural Downscaling for Complex Systems: from Large-scale to Small-scale by Neural Operator
Pengyu Lai, Jing Wang, Rui Wang +3
Predicting and understanding the chaotic dynamics in complex systems is essential in various applications. However, conventional approaches, whether full-scale simulations or small…
A Novel Paradigm in Solving Multiscale Problems
Jing Wang, Zheng Li, Pengyu Lai +5
Multiscale phenomena manifest across various scientific domains, presenting a ubiquitous challenge in accurately and effectively simulating multiscale dynamics in complex systems.…