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- W4386631375 abstract "In this paper, a partial element equivalent circuit (PEEC) informed deep learning approach is proposed for inverse electromagnetic scattering problems involving finite-sized dielectrics. To balance the enormous learning space and limited computation resources, a partial-relative-permittivity (PRP) model is proposed based on the compact PEEC model, which is capable to reveal the electromagnetic (EM) behavior of three-dimensional (3-D) heterogeneous structures by a 2.5-dimensional (2.5-D) representation. A tailored generative adversarial network (GAN) is developed to learn the intrinsic relation between the PRP model and the near-field information. To imitate the real-world phenomenon with adequate and accurate data, a dedicated training dataset is built numerically. With the GAN model, a PRP model can be interpreted from the measured phaseless magnetic fields at a single frequency, resulting in a broad-band c-PEEC model that is especially useful in power integrity and radiated emission analysis." @default.
- W4386631375 created "2023-09-13" @default.
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- W4386631375 date "2023-07-29" @default.
- W4386631375 modified "2023-10-18" @default.
- W4386631375 title "A PEEC-informed Deep Learning Approach for Inverse Electromagnetic Problem in Heterogeneous Integrations" @default.
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- W4386631375 doi "https://doi.org/10.1109/emcsipi50001.2023.10241652" @default.
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