SP-ADMM learns energy-stable derivative stencils for Maxwell equations from noisy data by enforcing skew-adjointness through reduced parameterization of periodic convolution stencils.
Pic 2o-sim: A physics- inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device fdtd simulation.arXiv preprint arXiv:2406.17810, 2024
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An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations
SP-ADMM learns energy-stable derivative stencils for Maxwell equations from noisy data by enforcing skew-adjointness through reduced parameterization of periodic convolution stencils.