Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models

We designed mirrored plasmonic filters using an advanced active machine learning algorithm that efficiently explores multiple physical models with different approximation fidelities and costs. This method is applicable to a variety of nanophotonics optimization problems.