Numerical methods for PDE constrained optimization with uncertain data. Abstracts from the workshop held January 27 -- February 2, 2013.
type: Item
instance of: scholarly article
title: Numerical methods for PDE constrained optimization with uncertain data. Abstracts from the workshop held January 27 -- February 2, 2013.
zbMATH Open document ID: 1349.00187
DOI: 10.4171/OWR/2013/04
published in: Oberwolfach Reports
publication date: 2016-11-27T00:00:00Z
review text: Summary: Optimization problems governed by partial differential equations (PDEs) arise in many applications in the form of optimal control, optimal design, or parameter identification problems. In most applications, parameters in the governing PDEs are not deterministic, but rather have to be modeled as random variables or, more generally, as random fields. It is crucial to capture and quantify the uncertainty in such problems rather than to simply replace the uncertain coefficients with their mean values. However, treating the uncertainty adequately and in a computationally tractable manner poses many mathematical challenges. The numerical solution of optimization problems governed by stochastic PDEs builds on mathematical subareas, which so far have been largely investigated in separate communities: Stochastic Programming, Numerical Solution of Stochastic PDEs, and PDE Constrained Optimization. The workshop achieved an impulse towards cross-fertilization of those disciplines which also was the subject of several scientific discussions. It is to be expected that future exchange of ideas between these areas will give rise to new insights and powerful new numerical methods.
Mathematics Subject Classification ID: 00B05
Mathematics Subject Classification ID: 00B25
Mathematics Subject Classification ID: 65-06
Mathematics Subject Classification ID: 35-06
Mathematics Subject Classification ID: 49-06
Mathematics Subject Classification ID: 60-06
Mathematics Subject Classification ID: 35Q93
Mathematics Subject Classification ID: 49M25
Mathematics Subject Classification ID: 60Hxx
Mathematics Subject Classification ID: 65M75
Mathematics Subject Classification ID: 65N75
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 6656867
Wikidata QID: Q58047880
MaRDI profile type: Publication
cites work: A Fokker-Planck control framework for multidimensional stochastic processes
cites work: OPTIMAL CONTROL OF PROBABILITY DENSITY FUNCTIONS OF STOCHASTIC PROCESSES
full work available at URL: https://doi.org/10.4171/owr/2013/04
OpenAlex ID: W1996416249
description: scientific article; zbMATH DE number 6656867