ArticleScientific reports2024
Physics-constrained machine learning for electrodynamics without gauge ambiguity based on Fourier transformed Maxwell's equations.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Physics-informed deep learning-based adaptive beamforming for phased array weather radar.Scientific reports · 2026Article
- Fast and accurate extraction of microwave filter coupling matrix via physics-informed deep learning.Scientific reports · 2026Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge.Scientific reports · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
We utilize a Fourier transformation-based representation of Maxwell's equations to develop physics-constrained neural networks for electrodynamics without gauge ambiguity, which we label the Fourier-Helmholtz-Maxwell neural operator method. In this approach, both of Gauss's laws and Faraday's law are built in as hard constraints, as well as the longitudinal component of Ampère-Maxwell in Fourier space, assuming the continuity equation. An encoder-decoder network acts as a solution operator for the transverse components of the Fourier transformed vector potential,
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