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20092026
most citedSolving high-dimensional partial differential equations using deep learning

1.8k citations · 7.5k across the 146 of their papers we have counts for

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Showing 2010Show all

5 papers · 1 filter

math.NA2010★ 443 cited

Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients

Martin Hutzenthaler, Arnulf Jentzen, Peter E. Kloeden

On the one hand, the explicit Euler scheme fails to converge strongly to the exact solution of a stochastic differential equation (SDE) with a superlinearly growing and globally on…

math.PR2010★ 47 cited

Taylor expansions of solutions of stochastic partial differential equations with additive noise

Arnulf Jentzen, Peter Kloeden

The solution of a parabolic stochastic partial differential equation (SPDE) driven by an infinite-dimensional Brownian motion is in general not a semi-martingale anymore and does i…

math.PR2010★ 68 cited

A mild Ito formula for SPDEs

Giuseppe Da Prato, Arnulf Jentzen, Michael Roeckner

This article introduces a certain class of stochastic processes, which we suggest to call mild Ito processes, and a new - somehow mild - Ito type formula for such processes. Exampl…

math.PR2010★ 71 cited

Regularity analysis for stochastic partial differential equations with nonlinear multiplicative trace class noise

Arnulf Jentzen, Michael Roeckner

In this article spatial and temporal regularity of the solution process of a stochastic partial differential equation (SPDE) of evolutionary type with nonlinear multiplicative trac…

math.NA2010★ 71 cited

A Milstein scheme for SPDEs

Arnulf Jentzen, Michael Roeckner

This article studies an infinite dimensional analog of Milstein's scheme for finite dimensional stochastic ordinary differential equations (SODEs). The Milstein scheme is known to…