Date of Award
Summer 2025
Document Type
Open Access Thesis
Department
Computer Science and Engineering
First Advisor
Jason Bakos
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.
Rights
© 2025, James Clayton Crews
Recommended Citation
Crews, J. C.(2025). Hardware Accelerated Simulation of Buck Converters Using Physics-Informed Neural Networks. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/8443