We propose a numerical method to solve parameter-dependent hyperbolic pa...
Probabilistic variants of Model Order Reduction (MOR) methods have recen...
We consider the problem of state estimation from m linear measurements,
...
Most common Optimal Transport (OT) solvers are currently based on an
app...
This paper is concerned with convergence estimates for fully discrete tr...
In this paper, we propose new learning algorithms for approximating
high...
The performance of projection-based model order reduction methods for so...
We study the approximation of multivariate functions with tensor network...
We consider approximation rates of sparsely connected deep rectified lin...
This paper considers the problem of maximizing an expectation function o...
In this paper, we propose and analyze a model selection method for tree
...
We study the approximation by tensor networks (TNs) of functions from
cl...
We study the approximation of functions by tensor networks (TNs). We sho...
In this paper, we propose a geometry based algorithm for dynamical low-r...
Under certain conditions, an element of a tensor product space can be
id...
We consider the problem of the estimation of a high-dimensional probabil...
This paper is concerned with the approximation of a function u in a give...
A methodology for using random sketching in the context of model order
r...
This paper is concerned with the approximation of high-dimensional funct...
In this paper, we propose a low-rank approximation method based on discr...