SP-ADMM learns energy-stable derivative stencils for Maxwell equations from noisy data by enforcing skew-adjointness through reduced parameterization of periodic convolution stencils.
Distributed optimization and statistical learning via the alternating direction method of multipliers.Foun- dations and Trends in Machine Learning, 3(1):1–122, 2011
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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.