Wankerl, Heribert and Stern, Maike L. and Mahdavi, Ali and Eichler, Christoph and Lang, Elmar W. (2021) Parameterized reinforcement learning for optical system optimization. JOURNAL OF PHYSICS D-APPLIED PHYSICS, 54 (30): 305104. ISSN 0022-3727, 1361-6463
Full text not available from this repository. (Request a copy)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 system design. However, if the corresponding inverse design problem can be reformulated as a parameterized Markov decision process, reinforcement learning (RL) provides a heuristic framework to solve it. In this work, we use multi-layer thin films as an example of the aforementioned optimization problems and consider three design parameters: Each of the thin film layer's dielectric material (discrete) and thickness (continuous), as well as the total number of layers (discrete). While recent methods merely determine the optimal thicknesses and-less commonly-the layers' materials, our approach optimizes the total number of stacked layers as well. In summary, we further develop a Q-learning variant to solve inverse design optimization and thereby outperform human experts and current approaches like needle-point optimization or naive RL. For this purpose, we propose an exponentially transformed reward signal that eases policy search and enables constrained optimization. Moreover, the learned Q-values contain information about the optical properties of multi-layer thin films, which allows us a physical interpretation or what-if analysis and thus enables explainability.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | REFRACTIVE-INDEX PROFILE; LIGHT-EMITTING-DIODES; DEEP NEURAL-NETWORKS; ANTIREFLECTION COATINGS; INVERSE DESIGN; GO; machine learning; reinforcement learning; inverse design problem; optics; multi-layer thin-film; optimization |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 05 Jul 2022 06:23 |
| Last Modified: | 05 Jul 2022 06:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/45581 |
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