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13 March 2024 Machine learning-enabled photonic simulation
Chenkai Mao, Mingkun Chen, Robert C. Lupoiu, Jonathan A. Fan
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030H (2024) https://doi.org/10.1117/12.3004505
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
Neural network-based surrogate partial differential equation (PDE) solvers are of great interest due to their potential to solve PDEs and related inverse problems in a fast and efficient manner. However, these end-to-end methods largely remain limited to predetermined problem sizes and fixed PDE parameters, preventing their application to practical simulation tasks that comprise heterogeneous PDE parameters and arbitrary domain sizes. We propose Specialized Neural Accelerator-Powered Domain Decomposition Methods (SNAP-DDM), which is a DDM-based approach to PDE problem solving in which an ensemble of specialized neural operators is trained to accelerate the solving of subdomain problems containing arbitrary boundary conditions and geometric parameters. We tailor SNAP-DDM to 2D electromagnetics problems and show how innovations in network architecture and loss function engineering can enable trained SNAP-DDM subdomain solvers with over 99\% accuracy. We also show how SNAP-DDM can be used to accurately solve a wide range of electromagnetics scattering problems in time scales comparable to traditional FDFD algorithms.
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Chenkai Mao, Mingkun Chen, Robert C. Lupoiu, and Jonathan A. Fan "Machine learning-enabled photonic simulation", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030H (13 March 2024); https://doi.org/10.1117/12.3004505
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KEYWORDS
Boundary conditions
Education and training
Dielectrics
Silicon
Electromagnetism
Data modeling
Electromagnetic simulation
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