Date of Award
January 2016
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mechanical Engineering
First Advisor
Karthik Ramani
Committee Member 1
David Gleich
Committee Member 2
Niklas Elmqvist
Committee Member 3
Jitesh Panchal
Abstract
Graphs are central tools to aid our understanding of biological, physical, and social systems. Graphs also play a key role in representing and understanding the visual world around us, 3D-shapes and 2D-images alike. In this dissertation, I propose the use of physical or natural phenomenon to understand graph structure. I investigate four phenomenon or laws in nature: (1) Brownian motion, (2) Gauss's law, (3) feedback loops, and (3) neural synapses, to discover patterns in graphs.
Recommended Citation
Sinha, Ayan, "Physics based supervised and unsupervised learning of graph structure" (2016). Open Access Dissertations. 1397.
https://docs.lib.purdue.edu/open_access_dissertations/1397
