Accelerating Neural PDE Solvers: Pre-generated Data for Few-Shot Learning

This research focuses on improving the efficiency of neural PDE solvers through the use of pre-generated data for few-shot learning scenarios. The approach has the potential to significantly reduce the computational cost and time required for solving partial differential equations in various scientific and engineering applications.

• •Pre-generated data is used to enhance few-shot learning for neural PDE solvers.
• •The method aims to reduce computational costs in PDE solving.
• •Potential applications span scientific and engineering domains.