Genetic Algorithms Applied to GaN Power Transistors
Marco Chakraborty
Corresponding 0000-0001-2744-8606
Bangladesh University of Engineering and Technology
Rifat Mensah
Korea Advanced Institute of Science and Technology
Priya Li
Tribhuvan University
Li Adeyemi
Chittagong University of Engineering & Technology
Abstract
Increasing deployment of flexible pavements has exposed clear limitations in existing approaches to GaN power transistors. The framework was implemented and its predictions benchmarked using genetic algorithms. The hybrid model reduced root-mean-square error by 19% over the physics-only formulation. The results underscore the importance of long-term monitoring for reliable performance assessment.
Keywords:
GaN power transistorsgenetic algorithmsenergy storagestate of charge estimationnanocompositesdeep learning
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