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Data assimilation is a well-established technique that combines computational models with observational data and is now widely used in a variety of fields and holds great promise for many digital twin applications. It refers to the estimation of the state of a physical system from models and measurements by fitting models of physical systems to data. In this talk, we introduce our new trial for internal structure modeling of waveguide devices using data assimilation. Our proposed approach can evaluate the waveguide structure by fitting models of waveguide structures to a measured nonlinear spectral change induced by optical pulse propagation.
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Tsuyoshi Konishi, Junpei Funatsuki, Tomoya Kurahashi, "Data assimilation-based internal structure modeling for waveguide devices," Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380U (15 March 2023); https://doi.org/10.1117/12.2656521