  This paper proposes PE-GPT, a custom-tailored large language model uniquely
adapted for power converter modulation design. By harnessing in-context
learning and specialized tiered physics-informed neural networks, PE-GPT guides
users through text-based dialogues, recommending actionable modulation
parameters. The effectiveness of PE-GPT is validated through a practical design
case involving dual active bridge converters, supported by hardware
experimentation. This research underscores the transformative potential of
large language models in power converter modulation design, offering enhanced
accessibility, explainability, and efficiency, thereby setting a new paradigm
in the field.
