Journal Papers
*D. Z. Huang, N. H. Nelsen, and M. Trautner. "An operator learning perspective on parameter-to-observable maps." Foundations of Data Science. 2024. (publication) (arxiv)
*K. Bhattacharya, N. Kovachki, A. Rajan, A. M. Stuart, and M. Trautner. "Learning Homogenization for Elliptic Operators." SIAM Journal on Numerical Analysis. 2024. (publication) (arxiv)
B. Liu, E. Ocegueda, M. Trautner, A. M. Stuart, and K. Bhattacharya. "Learning Macroscopic Internal Variables and History Dependence from Microscopic Models." Journal of the Mechanics and Physics of Solids. 2023.
*K. Bhattacharya, B. Liu, A. Stuart, and M. Trautner. "Learning Homogenized Markovian Models in Viscoelasticity." SIAM Multiscale Modeling & Simulation. 2023.
*denotes alphabetical order
Conference Papers
M. Trautner, G. Margolis, and S. Ravela, "Informative Neural Ensemble Kalman Learning." DDDAS. 2020. (arxiv)
Preprints
*P. Grohs, S. Lanthaler, and M. Trautner. "Theory-to-practice gap for neural networks and neural operators." 2025. (arXiv)
*K. Bhattacharya, L. Cao, G. Stepaniants, A. M. Stuart, and M. Trautner. "Learning Memory and Material Dependent Constitutive Laws." 2025. (arXiv)
*S. Lanthaler, A. M. Stuart, and M. Trautner. "Discretization error of Fourier neural operators." 2024. (arXiv)
*denotes alphabetical order
Awards
USACM Travel Award for WCCM-PANACM. (2024).
Thomas A. Tisch Prize for Graduate Teaching in Mathematics (Caltech, 2022)
Rhodes Scholarship Finalist (2020)
NCAA Elite 90 Award for highest GPA and credit-hours among national track championship qualifiers (2019)
MathWorks Math Modeling Challenge National Champion (2016)