T. Nguyen

activity

20162025

most citedA proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations

84 citations · 138 across the 17 of their papers we have counts for

collaborators

Showing 2019Show all

3 papers · 1 filter

math.AP2019

A Liouville principle for the random conductance model under degenerate conditions

Tuan Anh Nguyen

We consider a random conductance model on the $d$ -dimensional lattice, $d \in [2, \infty) \cap N$ , where the conductances take values in $(0, \infty)$ and are however not assume…

math.NA2019

An $L^{p}$ -comparison, $p \in (1, \infty)$ , on the finite differences of a discrete harmonic function at the boundary of a discrete box

Tuan Anh Nguyen

It is well-known that for a harmonic function $u$ defined on the unit ball of the $d$ -dimensional Euclidean space, $d \geq 2$ , the tangential and normal component of the gradient $\…

math.NA2019★ 84 cited

A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations

Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse +1

Deep neural networks and other deep learning methods have very successfully been applied to the numerical approximation of high-dimensional nonlinear parabolic partial differential…

A Liouville principle for the random conductance model under degenerate conditions

An Lp-comparison, p∈(1,∞), on the finite differences of a discrete harmonic function at the boundary of a discrete box

A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations

An $L^{p}$ -comparison, $p \in (1, \infty)$ , on the finite differences of a discrete harmonic function at the boundary of a discrete box