About Me
I'm Grant Norman, a PhD Candidate in the UQ and Data-Driven Modeling Group at the University of Colorado Boulder. My current research uses machine learning to discover partial differential equations from data. I'm also broadly interested in machine learning, uncertainty quantification and numerical methods.
Before beginning my PhD, I earned bachelor's degrees in both applied mathematics and aerospace engineering — both within three years. During that time, I investigated a problem in turbulence modeling within the Computational Mechanics and Geometry Laboratory, introduced a photogrammetry measurement methodology at a local space company, and worked on a virtual reality system at Lockheed Martin.
About this Site
After the culmination of my PhD course work in 2023, I started this collection of notes primarily as a reference for when I inevitably forget ideas but also as a means to encourage myself to keep learning in areas only tangentially related to my research. Since then it has evolved into my preferred method for organizing my thoughts on concepts that involve at least a few papers. In particular, Obsidian's internal linking system facilitates drawing connections between ideas, which is both satisfying and helpful for a broader understanding.
This site consists almost entirely of individual "concept notes" which are linked to one another. Each one of these notes is certainly less developed than individual blog posts, but hopefully together they can provide a bigger picture. I provide references to the sources I used, but in many cases, the information is general enough to be found on a similarly-named Wikipedia page, which I do not explicitly cite.
I suggest the following concept notes as starting points