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In the realm of nanophotonics, establishing the intricate relationship between design parameters and the ultimate response of a given nanophotonic devices stands as a formidable challenge. The prevalent utilization of numerical solutions to Maxwell's equations, whether through in-house codes or commercial software, often conceals the underlying physics. In this talk, we present machine-learning (ML) algorithms for elucidating the connection between design parameters and device response. We discuss two distinct ML methods to discern the roles and significance of individual design parameters, namely SHAP (SHapley Additive exPlanations) values and Pruning. By scrutinizing two diverse nano-devices using these complementary techniques, this talk sheds light on the compelling insights derived from this innovative approach.
Javani et al. (Tue,) studied this question.
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