# Dynamically orthogonal tensor methods for high-dimensional nonlinear PDEs

## TL;DR

This paper introduces dynamically orthogonal tensor methods that hierarchically decompose high-dimensional nonlinear PDEs, enabling efficient solution computation without rank reduction, demonstrated through numerical examples.

## Contribution

The paper presents a novel hierarchical tensor decomposition approach with dynamic orthogonality for solving high-dimensional nonlinear PDEs, avoiding traditional rank reduction techniques.

## Key findings

- Effective high-dimensional PDE solutions demonstrated
- Hierarchical tensor formats enable efficient computation
- New algorithms for mode management introduced

## Abstract

We develop new dynamically orthogonal tensor methods to approximate multivariate functions and the solution of high-dimensional time-dependent nonlinear partial differential equations (PDEs). The key idea relies on a hierarchical decomposition of the approximation space obtained by splitting the independent variables of the problem into disjoint subsets. This process, which can be conveniently be visualized in terms of binary trees, yields series expansions analogous to the classical Tensor-Train and Hierarchical Tucker tensor formats. By enforcing dynamic orthogonality conditions at each level of binary tree, we obtain coupled evolution equations for the modes spanning each subspace within the hierarchical decomposition. This allows us to effectively compute the solution to high-dimensional time-dependent nonlinear PDEs on tensor manifolds of constant rank, with no need for rank reduction methods. We also propose new algorithms for dynamic addition and removal of modes within each subspace. Numerical examples are presented and discussed for high-dimensional hyperbolic and parabolic PDEs in bounded domains.