Parameterized reinforcement learning for optical system optimization

URN to cite this document:: urn:nbn:de:bvb:355-epub-463244
DOI to cite this document:: 10.5283/epub.46324

Wankerl, Heribert

; Stern, Maike L.

; Mahdavi, Ali ; Eichler, Christoph ; Lang, Elmar W.

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Date of publication of this fulltext: 12 Jul 2021 08:21

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Abstract

Engineering a physical system to feature designated characteristics states an inverse design problem, which is often determined by several discrete and continuous parameters. If such a system must feature a particular behavior, the mentioned combination of both, discrete and continuous, parameters results in a challenging optimization problem that requires an extensive search for an optimal ...