Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning

This paper introduces a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision, particularly under data scarcity. It uses a learnable blending neuron to dynamically adjust term contributions based on their uncertainties and integrates transfer learning for enhanced efficiency.