Doctoral Dissertations
Date of Award
8-2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Industrial Engineering
Major Professor
Bing Yao
Committee Members
Anahita Khojandi, Zhongshun Shi, Xiaopeng Zhao
Abstract
The rapid advances in sensing technology have created a data-rich environment that tremendously
benefits predictive modeling and decision-making for complex systems. Harnessing
the full potential of this complexly-structured sensing data requires the development of
novel and reliable analytical models and tools for system informatics. Such advancements in
sensing present unprecedented opportunities to investigate system dynamics and optimize
decision-making processes for smart health. Nevertheless, sensing data is typically
characterized by high dimensionality and intricate structures. To fully unlock the potential of
this data, we significantly rely on innovative analytical methods and tools that can effectively
process information.
The objective of this dissertation is to develop innovative physics-augmented methodologies
for modeling, monitoring, and optimizing complex systems. Specifically, the research
focuses on the development of physics-augmented machine learning models that facilitate
optimal decision-making in complex healthcare systems. This research will enable 1) robust
predictive modeling of spatiotemporal systems; 2) extracting important feature information
about system dynamics; 3) optimizing decision-making under uncertainty and sparse sensor
observations. My research accomplishments include:
• Physics-constrained deep learning for robust inverse modeling of spatiotemporal systems:
In Chapter 2, we developed a physics-constrained machine learning framework
to address the high-dimensional inverse problem. This method integrates physics-based
principles with the advanced deep learning infrastructure to predict the spatiotemporal
system dynamics based on indirect and noisy sensor observations. This methodology
is implemented in inverse electrocardiography (ECG) modeling.
• Physics-constrained active learning and sequential design in 3D spatiotemporal systems:
In Chapter 3, a novel physics-constrained strategy for optimal sensor placement
is proposed to actively explore and model the dynamics of 3D complex-structured
systems. This active learning scheme combines uncertainty estimation in deep learning
and space-filling design over the complex geometry, enabling effective learning of system
dynamics from limited sensor exploration.
• Defects identification in 3D heterogeneous complex systems: In Chapter 4, we designed
a new framework to characterize the location and extent of diseased cardiac tissue
from indirect and noisy body sensor observations. Specifically, a hierarchical Gaussian
Process-based active learning framework was developed to reliably identify the defect
location and further extract spatial-varying properties in a complex 3D system.
Recommended Citation
Xie, Jianxin, "Physics-Augmented Modeling and Optimization of Complex Systems: Healthcare Applications. " PhD diss., University of Tennessee, 2023.
https://trace.tennessee.edu/utk_graddiss/8671