Degree Name
MS (Master of Science)
Program
Computer Science
Date of Award
5-2025
Committee Chair or Co-Chairs
Chelsie Dubay
Committee Members
Brian Bennett, Jeff Knisley, Dillon Buchanan, Matthew Harrison
Abstract
Physics-informed deep learning is a methodology in artificial intelligence aimed at combating the large training data requirement and the barrier of domain awareness that deep learning architectures commonly face in applications. Stochastic modeling integrated into the predictive models provides that domain knowledge. Variations of the Intelligent Driving Model impact the learning behaviors of the joint-training architecture. This thesis examines the effect of substituting the standard linear Intelligent Driving Model with a modified nonlinear version, as applied to real human driving behavior on the I-80 interstate. The experimentation also critically evaluates the complications that impede the viability of this architecture in application. The code for the following projects can be found at https://github.com/Glodanale/PILSTM.git
Document Type
Thesis - unrestricted
Recommended Citation
Glover, Alex, "Learning Behaviors in Physics-Informed Deep Learning" (2025). Electronic Theses and Dissertations. Paper 4541. https://dc.etsu.edu/etd/4541
Copyright
Copyright by the authors.
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons