Learning Time-Dependent PDEs: A Novel Neural Operator Approach

This research explores a novel neural operator for learning time-dependent partial differential equations (PDEs), a critical area for scientific computing and modeling. The inverse scattering inspiration and Fourier neural operator methodology suggest a potentially efficient and accurate approach to handling complex dynamics.

• •The paper introduces a new method leveraging the Fourier Neural Operator framework.
• •It addresses the challenge of learning time-dependent PDEs, important for various scientific simulations.
• •The approach draws inspiration from inverse scattering techniques, potentially improving performance.