Many phenomena in physics, including light, water waves, and sound, are described by wave equations. Given their coefficients, wave equations can be solved to high accuracy, but the presence of the wavelength scale often leads to large computer simulations for anything beyond the simplest geometries. The inverse problem, determining the coefficients from a field on a boundary, is even more demanding, since traditional optimization requires a large number of forward problems be solved sequentially. Here we show that the free-form inverse problem of wave equations can be solved with machine learning. First we show that deep neural networks can be used to predict the optical properties of nanostructured materials such as metasurfaces. Then we demonstrate the free-form inverse design of such nanostructures and show that constraints imposed by experimental feasibility can be taken into account. Our neural networks promise automated design in several technologies based on the wave equation.
We show that the free-form inverse design of nanophotonic matasurfaces can be solved with a modified CGAN machine learning method that balances the accuracy of desired optical properties with experimental feasibility.
We show that a modified CGAN machine learning method that balances the accuracy of desired optical properties with experimental feasibility can solve the free-form inverse design of nanophotonic matasurfaces.
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