Title: Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Power Grid Analysis, Graph Neural Networks, Generalization, Physics-Informed Neural Networks

Abstract: Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (GNNs), offer significant speedups but suffer from poor generalization when trained on limited or incomplete data. In this work, we systematically investigate the role of inductive biases in improving a model's ability to reliably learn power flow. Specifically, we evaluate three physics-informed strategies: (i) power-flow-constrained loss functions, (ii) complex-valued neural networks, and (iii) residual-based task reformulation. Using the ENGAGE dataset, which spans multiple low- and medium-voltage grid configurations, we conduct controlled experiments to isolate the effect of each inductive bias and assess both standard predictive performance and out-of-distribution generalization. Our study provides practical insights into which model assumptions most effectively guide learning for reliable and efficient voltage prediction in modern distribution networks.

TL;DR: We evaluate physics-informed inductive biases (constrained losses, complex-valued models, and residual reformulations) for enhancing standard predictive accuracy and out-of-distribution generalization of GNNs in voltage prediction tasks.