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

17 papers

math.NA2025

Full history recursive multilevel Picard approximations suffer from the curse of dimensionality for the Hamilton-Jacobi-Bellman equation of a stochastic control problem

Martin Hutzenthaler, Tuan Anh Nguyen

Full history recursive multilevel Picard (MLP) approximations have been proved to overcome the curse of dimensionality in the numerical approximation of semilinear heat equations w…

math.DG2022★ 1 cited

Foliations Formed by Generic Coadjoint Orbits of Lie Groups Corresponding to a Class Seven-Dimensional Solvable Lie Algebras

Tuyen T. M. Nguyen, Vu A. Le, Tuan A. Nguyen

We consider all connected and simply connected 7-dimensional Lie groups whose Lie algebras have nilradical $\g_{5,2} = \s \{X_1, X_2, X_3, X_4, X_5 \colon [X_1, X_2] = X_4, [X_1, X…

math.PR2022

A path-dependent stochastic Gronwall inequality and strong convergence rate for stochastic functional differential equations

Martin Hutzenthaler, Tuan Anh Nguyen

We derive a stochastic Gronwall lemma with suprema over the paths in the upper bound of the assumed affine-linear growth assumption. This allows applications to Itô processes with…

math.PR2022★ 2 cited

Multilevel Picard approximations for high-dimensional decoupled forward-backward stochastic differential equations

Martin Hutzenthaler, Tuan Anh Nguyen

Backward stochastic differential equations (BSDEs) appear in numeruous applications. Classical approximation methods suffer from the curse of dimensionality and deep learning-based…

math.NA2022

Multilevel Picard approximations of high-dimensional semilinear partial differential equations with locally monotone coefficient functions

Martin Hutzenthaler, Tuan Anh Nguyen

The full history recursive multilevel Picard approximation method for semilinear parabolic partial differential equations (PDEs) is the only method which provably overcomes the cur…

math.NA2021

Strong convergence rate of Euler-Maruyama approximations in temporal-spatial Hölder-norms

Tuan Anh Nguyen, Martin Hutzenthaler

Classical approximation results for stochastic differential equations analyze the -distance between the exact solution and its Euler-Maruyama approximations. In this article w…

math.NA2021

Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations

Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse +1

Backward stochastic differential equations (BSDEs) belong nowadays to the most frequently studied equations in stochastic analysis and computational stochastics. BSDEs in applicati…

math.PR2021

On the speed of convergence of Picard iterations of backward stochastic differential equations

Martin Hutzenthaler, Thomas Kruse, Tuan Anh Nguyen

It is a well-established fact in the scientific literature that Picard iterations of backward stochastic differential equations with globally Lipschitz continuous nonlinearity conv…

math.PR2021★ 15 cited

Multilevel Picard approximations for McKean-Vlasov stochastic differential equations

Martin Hutzenthaler, Thomas Kruse, Tuan Anh Nguyen

In the literatur there exist approximation methods for McKean-Vlasov stochastic differential equations which have a computational effort of order . In this article we introduce…

math.RA2021★ 2 cited

Testing isomorphism of complex and real Lie algebras

Tuan A. Nguyen, Vu A. Le, Thieu N. Vo

In this paper, we give algorithms for determining the existence of isomorphism between two finite-dimensional Lie algebras and compute such an isomorphism in the affirrmative case.…

math.NA2020★ 10 cited

Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities

Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse +1

The recently introduced full-history recursive multilevel Picard (MLP) approximation methods have turned out to be quite successful in the numerical approximation of solutions of h…

math.RA2020

On the problem of classifying solvable Lie algebras having small codimensional derived algebras

Hoa Q. Duong, Vu A. Le, Tuan A. Nguyen +2

This paper concerns the problem of classifying finite-dimensional real solvable Lie algebras whose derived algebras are of codimension 1 or 2. On the one hand, we present an effect…

math.AP2019

A Liouville principle for the random conductance model under degenerate conditions

Tuan Anh Nguyen

We consider a random conductance model on the -dimensional lattice, , where the conductances take values in and are however not assume…

math.NA2019

An -comparison, , 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 defined on the unit ball of the -dimensional Euclidean space, , 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…

math.RA2018

On the classifying problem for the class of real solvable Lie algebras having 2-dimensional or 2-codimensional derived ideal

Vu A. Le, Tuan A. Nguyen, Tu T. C. Nguyen +2

Let denote the class of all -dimensional real solvable Lie algebras having -dimensional derived ideal (). In 199…

math.PR2016★ 24 cited

Quenched invariance principles for the random conductance model on a random graph with degenerate ergodic weights

Jean-Dominique Deuschel, Tuan Anh Nguyen, Martin Slowik

We consider a stationary and ergodic random field that is parameterized by the edge set of the Euclidean lattice , . The random varia…