2 papers
cs.LG2025
An Inverse Scattering Inspired Fourier Neural Operator for Time-Dependent PDE Learning
Rixin Yu
Learning accurate and stable time-advancement operators for nonlinear partial differential equations (PDEs) remains challenging, particularly for chaotic, stiff, and long-horizon d…
math.DS2024
Koopman Theory-Inspired Method for Learning Time Advancement Operators in Unstable Flame Front Evolution
Rixin Yu, Marco Herbert, Markus Klein +1
Predicting the evolution of complex systems governed by partial differential equations (PDEs) remains challenging, especially for nonlinear, chaotic behaviors. This study introduce…