A constrained least-squares (ADMM) learns a skew-symmetric derivative stencil for 1D Maxwell equations that conserves discrete energy exactly while matching centered-difference accuracy.
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An Energy Stable Approach for Learning Derivative Operators from Noisy Data for Maxwells Equations
A constrained least-squares (ADMM) learns a skew-symmetric derivative stencil for 1D Maxwell equations that conserves discrete energy exactly while matching centered-difference accuracy.