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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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License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 12 Jul 2021 08:21



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 ...

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