Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks

This paper proposes LAM-PINN, a compositional meta-learning framework designed to mitigate task heterogeneity in Physics-Informed Neural Networks (PINNs). It addresses the challenge of training PINNs for families of partial differential equations (PDEs) which often face high computational costs or negative transfer under data-scarce conditions.