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. 

DISSERTATION DFENSE

Author : Kaushik Roy
Advisor: Dr. Amit Sheth
Date: July 15, 2025
Time: 10:30 am
Place: Rm 529 AI Institute
Zoom Link : Join Zoom Meeting

Abstract

Neural‐network–driven artificial intelligence has achieved impressive predictive accuracy, yet its opaque, data-centric modus operandi still limits trustworthy deployment in safety-critical settings. A central barrier is the difficulty of aligning continuous representations learned by networks with the process knowledge -- formal, expert-crafted diagnostic or operational procedures that govern real-world decision making. This dissertation introduces Process Knowledge-Infused Learning and Reasoning (PK-iL), a neurosymbolic framework that injects task-specific process structures directly into end-to-end differentiable models. PK-iL marries symbolic constraints with gradient-based optimization, yielding predictors whose internal reasoning steps remain faithful to domain processes while retaining the adaptability and scale of modern deep learning.

The contributions are fourfold: (1) a formal representation for encoding process knowledge as differentiable constraints; (2) algorithms that integrate these constraints into training objectives and inference routines; (3) theoretical analysis showing how process alignment improves controllability and transparency without sacrificing expressivity; and (4) empirical validation in mental-health decision support, where psychiatric diagnostic criteria provide rigorous process ground truth. Across multiple datasets and baselines, PK-iL delivers higher diagnostic accuracy, markedly more evident explanation traces, and graceful handling of out-of-distribution cases, features essential for adoption as a human-AI “partner” in high-stakes workflows. These results demonstrate a viable path toward reliable, process-guided neurosymbolic AI.