AI RESEARCH
Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
arXiv CS.LG
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ArXi:2605.05217v1 Announce Type: new 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