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- W3211229402 abstract "In this paper, a deep learning-based macro circuit model approach for black-box electromagnetic compatibility (EMC) problems is proposed. The concept of the partial element equivalent circuit (PEEC) method is deployed in constructing the circuit topology in the full-space mesh of a black-box device. The mesh-based circuit model can serve as a powerful tool in solving the emission and immunity of the system-level EMC problems. A physics based deep neural network (DNN) is designed and optimized with the electromagnetic and circuit theories. The approach is validated by a proof-of-concept numerical example. The training and validation data are obtained by solving simplified PEEC models of randomly generated routes on a pre-defined mesh set of a black box problem. Good agreement and efficiency are observed." @default.
- W3211229402 created "2021-11-08" @default.
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- W3211229402 date "2021-07-26" @default.
- W3211229402 modified "2023-10-16" @default.
- W3211229402 title "A Deep Learning Based Macro Circuit Modeling for Black-box EMC Problems" @default.
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- W3211229402 doi "https://doi.org/10.1109/emc/si/pi/emceurope52599.2021.9559203" @default.
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