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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 2013Show all

5 papers · 1 filter

math.NA2013★ 32 cited

An exponential Wagner-Platen type scheme for SPDEs

Sebastian Becker, Arnulf Jentzen, Peter E. Kloeden

The strong numerical approximation of semilinear stochastic partial differential equations (SPDEs) driven by infinite dimensional Wiener processes is investigated. There are a numb…

math.NA2013★ 66 cited

Exponential integrability properties of numerical approximation processes for nonlinear stochastic differential equations

Martin Hutzenthaler, Arnulf Jentzen, Xiaojie Wang

Exponential integrability properties of numerical approximations are a key tool for establishing positive rates of strong and numerically weak convergence for a large class of nonl…

math.PR2013★ 58 cited

Local Lipschitz continuity in the initial value and strong completeness for nonlinear stochastic differential equations

Sonja Cox, Martin Hutzenthaler, Arnulf Jentzen

Recently, Hairer et. al (2012) showed that there exist SDEs with infinitely often differentiable and globally bounded coefficient functions whose solutions fail to be locally Lipsc…

math.NA2013★ 104 cited

Galerkin approximations for the stochastic Burgers equation

Dirk Blömker, Arnulf Jentzen

Existence and uniqueness for semilinear stochastic evolution equations with additive noise by means of finite dimensional Galerkin approximations is established and the convergence…

math-ph2013★ 24 cited

Renormalized powers of Ornstein-Uhlenbeck processes and well-posedness of stochastic Ginzburg-Landau equations

E. Weinan, Arnulf Jentzen, Hao Shen

This article analyzes well-definedness and regularity of renormalized powers of Ornstein-Uhlenbeck processes and uses this analysis to establish local existence, uniqueness and reg…