Neural Operator-Based Surrogate Solver for Free-Form Electromagnetic Inverse Design
Yannick Augenstein, Taavi Repän, Carsten Rockstuhl
Abstract
Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep-learning techniques.
Topics & Concepts
SolverComputer scienceOperator (biology)ElectromagneticsContext (archaeology)Inverse problemComputational electromagneticsArtificial neural networkConvolutional neural networkInversePartial differential equationComputational scienceElectromagnetic fieldMathematical optimizationArtificial intelligencePhysicsMathematicsMathematical analysisRepressorBiologyPaleontologyEngineering physicsBiochemistryChemistryTranscription factorQuantum mechanicsProgramming languageGeneGeometryModel Reduction and Neural NetworksPhotonic and Optical DevicesOptical Coatings and Gratings