Journal Papers
*K. Bhattacharya, L. Cao, G. Stepaniants, A. M. Stuart, and M. Trautner. "Learning Memory and Material Dependent Constitutive Laws." SMAI Journal of Computational Mathematics. 2026. (publication) (pdf)
*D. Z. Huang, N. H. Nelsen, and M. Trautner. "An operator learning perspective on parameter-to-observable maps." Foundations of Data Science. 2024. (publication) (pdf)
*K. Bhattacharya, N. Kovachki, A. Rajan, A. M. Stuart, and M. Trautner. "Learning Homogenization for Elliptic Operators." SIAM Journal on Numerical Analysis. 2024. (publication) (pdf)
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. (pdf)
*K. Bhattacharya, B. Liu, A. Stuart, and M. Trautner. "Learning Homogenized Markovian Models in Viscoelasticity." SIAM Multiscale Modeling & Simulation. 2023. (pdf)
*denotes alphabetical order
Conference Papers
M. Trautner, G. Margolis, and S. Ravela, "Informative Neural Ensemble Kalman Learning." DDDAS. 2020. (arxiv)Â