27th Conference of the International Linear Algebra Society (ILAS 2026)

Derivative informed Tucker tensor train Taylor series surrogate models

May 20, 2026, 11:35 AM

25m

Torgersen Hall 1060 (Virginia Tech)

Minisymposium Talk Recent Advances in Tensor Decompositions for Model and Data Reduction Recent Advances in Tensor Decompositions for Model and Data Reduction

Speaker

Dr Nick Alger (University of Texas, Austin)

Description

We introduce Tucker tensor train Taylor series (T4S) surrogate models for high dimensional mappings that depend implicitly on the solution of a partial differential equation. Traditionally, Taylor series are intractable here because the derivative tensors are enormous, and are only accessible through multilinear actions. We overcome these challenges by approximating each derivative tensor with a Tucker decomposition composed with a tensor train, fitting each Tucker tensor train to symmetric tensor actions via Riemannian manifold optimization. We present theory and numerical experiments that validate the model and numerical method.