Author: James Crews
Advisor: Dr. Jason Bakos
Date: July 8, 2025
Time: 02:00 pm
Place: Teams
Meeting Link

Abstract

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly compute exact derivatives of higher-order differential equations through automatic differentiation. The effects of fixed-point quantization on the neural network using the high-level synthesis for machine learning (HLS4ML) framework are detailed and compared with numerical integration methods, discussing the trade-offs in latency, hardware efficiency, and prediction accuracy over transient- and steady-state converter operation.