Powerbrain: Design Automation

Documentation

Reference

1. F. Tian, D. B. Cobaleda and W. Martinez, "Automatic Data Extraction Based on Semiconductor Datasheet for Design Automation of Power Converters," 2022 International Power Electronics Conference (IPEC-Himeji 2022- ECCE Asia), Himeji, Japan, 2022, pp. 922-927, doi: 10.23919/IPEC-Himeji2022-ECCE53331.2022.9806859.

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2. F. Tian, S. Li, X. Ning, D.B. Cobaleda & W. Martinez, "Embedding-Encoded Artificial Neural Network Model for MOSFET Preselection: Integrating Analytic Loss Models with Dynamic Characteristics from Datasheets," IEEE Applied Power Electronics Conference & Exposition- APEC, pp.1-7, Long Beach-USA, Feb, 2024.

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3. F. Tian, H. Wouters, X. Shen & W. Martinez, "Optimizing DC Inductor Design with Air Gap for Triangular Excitation: A Reinforcement Learning Approach," IEEE Applied Power Electronics Conference & Exposition- APEC, pp. 1-6, Long Beach-USA, Feb, 2024.

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4. X. Shen, H. Wouters and W. Martinez, "Deep Neural Network for Magnetic Core Loss Estimation using the MagNet Experimental Database," 2022 24th European Conference on Power Electronics and Applications (EPE'22 ECCE Europe), Hanover, Germany, 2022, pp. 1-8.

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5. X. Shen and W. Martinez, "Machine Learning Model for High-Frequency Magnetic Loss Predictions Based on Loss Map by a Measurement Kit," 2023 25th European Conference on Power Electronics and Applications (EPE'23 ECCE Europe) , Aalborg, Denmark, 2023, pp. 1-8, doi: 10.23919/EPE23ECCEEurope58414.2023.10264272.

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