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- W4311461954 abstract "We propose a scientific machine learning (SciML) algorithm toward 3-D dynamic digital twins, which represent multiphysics coupling effects in large-scale electronic chips. The SciML is a burgeoning topic, and here we refer to an organic fusion of scientific computing, model order reduction, and machine learning (ML) method. The proposed model order reduction compresses multiphysics information by a data-driven non-intrusive technique, thus getting rid of the access to backend source code; the proposed ML intrinsically infers the partial differential equation operators encoding the physical process. Numerical experiments showcase that the proposed digital twins have superior properties in real-time intelligent computing and generalization capability in predictive modeling. Nevertheless, it should be mentioned that the presented work is still in an early stage of intelligent digital twins." @default.
- W4311461954 created "2022-12-26" @default.
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- W4311461954 date "2022-12-01" @default.
- W4311461954 modified "2023-10-18" @default.
- W4311461954 title "Scientific Machine Learning Enables Multiphysics Digital Twins of Large-Scale Electronic Chips" @default.
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- W4311461954 doi "https://doi.org/10.1109/tmtt.2022.3208917" @default.
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