PhD candidate at Caltech
I am a fourth 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 have done research on learning the structure of dynamical systems from data and scientific computing. Currently, I work on learning homogenized constitutive models, which is a setting where partial differential equations describe how multiscale materials respond to forcing. I also work on more general theory of operator learning such as learning rates and error bounds. 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'm currently collaborating with a group at Lawrence Livermore National Labs on reduced order modeling methods.
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.
Recent: New arXiv preprint on using operator learning for parameter-to-observable maps. I'll be talking about this at SIAM UQ! I'll also be at SIAM LA in May.
Almost recent: Check out our arXiv preprint on learning homogenization in the presence of discontinuous PDE coefficients!
trautner (at) caltech (dot) edu