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20182025
most citedA stochastic gradient method for a class of nonlinear PDE-constrained optimal control problems under uncertainty

1 citations · 5 across the 8 of their papers we have counts for

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Showing 2019Show all

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math.OC2019

A Stochastic Gradient Method with Mesh Refinement for PDE Constrained Optimization under Uncertainty

Caroline Geiersbach, Winnifried Wollner

Models incorporating uncertain inputs, such as random forces or material parameters, have been of increasing interest in PDE-constrained optimization. In this paper, we focus on th…

math.OC2019

Computational Aspects for Interface Identification Problems with Stochastic Modelling

Caroline Geiersbach, Estefania Loayza, Kathrin Welker

In this paper, a shape optimization problem constrained by a random elliptic partial differential equation with a pure Neumann boundary is presented. The model is motivated by appl…