[2605.05217] Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning

Computer Science > Machine Learning arXiv:2605.05217 (cs) [Submitted on 17 Apr 2026] Title:Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning Authors:Reza Pirayeshshirazinezhad View a PDF of the paper titled Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning, by Reza Pirayeshshirazinezhad View PDF HTML (experimental) Abstract:We propose a self-supervised physics-informed neural network (PINN) framework that adaptively balances physics-based and data-driven supervision for scientific machine learning under data scarcity. Unlike prior PINNs that rely on fixed or heuristic weighting of physics residuals and data loss, our approach introduces a learnable blending neuron that dynamically adjusts the relative contribution of each term based on their uncertainties. This mechanism enables stable training and improved generalization without manual tuning. To further enhance efficiency, we integrate a transfer learning strategy that reuses representations from related domains and adapts them to new physical systems with limited data. We validate the framework for the prediction of heat transfer in liquid-metal miniature heat sinks using only 87 CFD datapoints, where the adaptive PINN achieves an error <8%, outperforming shallow neural networks, kernel methods, and physics-only baselines. Our framework provides a general recipe for embedding physics adaptively into neural networks, offering a robust and reproducible ...