PhD student at Caltech
I am a third year PhD candidate in the Department of Computing + Mathematical Sciences at Caltech. My research interests lie at the intersection of computational science and applied mathematics. In the past, I've have done research on learning the structure of dynamical systems from data and scientific computing. Currently, I work on inverse problems for dynamical systems, specifically with partial differential equations describing how multiscale materials respond to forcing. At Caltech, I work in the group of Professor Andrew Stuart.
In Summer 2021, I worked at at Sandia National Labs researching quantum inference algorithms.
I am a recipient of the Department of Energy Computational Science Graduate Fellowship. I received my SB degree from MIT in Mathematics with minors in computer science and mechanical engineering in 2020.
Check out the most recent arXiv preprint here: "Learning Markovian Homogenized Models in Viscoelasticity." This work will appear in SIAM Multiscale Modeling and Simulation in 2023.
April 12, 2023: I passed my candidacy! The title of my presentation was "Operator Learning for Continuum Mechanics and PDEs."
trautner (at) caltech (dot) edu