SP-ADMM learns energy-stable derivative stencils for Maxwell equations from noisy data by enforcing skew-adjointness through reduced parameterization of periodic convolution stencils.
Reynolds averaged turbulence mod- elling using deep neural networks with embedded invariance.Journal of Fluid Mechanics, 807:155–166, 2016
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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.