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cs.LG2026
From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs
Pengyu Lai, Yixiao Chen, Dewu Yang +3
Partial differential equations (PDEs) are fundamental for modeling complex physical systems, yet classical numerical solvers face prohibitive computational costs in high-dimensiona…
cs.LG2025
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…
cs.LG2025
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…