Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
Authors: Dan DeGenaro, Xin Li, Obed Amo, Michael Pokojovy, Sarah Adel Bargal, Markus Lange-Hegermann, Bogdan Raiţă
Organizations: Department of Computer Science, Georgetown University · Department of Mathematics, Georgetown University · Department of Mathematics and Statistics, Old Dominion University · School of Data Science, Old Dominion University · Institute Industrial IT, Department of Computer Science and Automation, OWL University of Applied Sciences and Arts
Abstract
We introduce FLASH-MAX, a shallow, exact-by-construction neural network architecture for predicting homogeneous electromagnetic fields from sparse pointwise observations. Each hidden neuron represents a separate exact solution to Maxwell's equations, so that the network satisfies the governing equations symbolically by construction and can be trained end-to-end from sparse data within seconds. We prove a universal approximation result showing that this exact model class remains universal on arbitrary domains. FLASH-MAX reaches sub-1% relative validation error from about 1K sparse pointwise observations in seconds, all while maintaining a zero PDE residual, and keeps single-digit errors even for only 100 observations sampled from 3D space. These results suggest that moving governing structure from the loss into the hypothesis class can dramatically improve the trade-off between precision and optimization speed in scientific machine learning.