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pro vyhledávání: '"ZANDER, ELMAR"'
Scientific computations or measurements may result in huge volumes of data. Often these can be thought of representing a real-valued function on a high-dimensional domain, and can be conceptually arranged in the format of a tensor of high degree in s
Externí odkaz:
http://arxiv.org/abs/1906.05669
The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updatin
Externí odkaz:
http://arxiv.org/abs/1611.09293
When a mathematical or computational model is used to analyse some system, it is usual that some parameters resp.\ functions or fields in the model are not known, and hence uncertain. These parametric quantities are then identified by actual observat
Externí odkaz:
http://arxiv.org/abs/1606.09440
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. In the form of conditional expectation the Bayesian update becomes comp
Externí odkaz:
http://arxiv.org/abs/1511.00524
Publikováno v:
In Journal of Computational Physics 1 June 2020 410
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 March 2014 270:247-269
Autor:
Matthies, Hermann G., Zander, Elmar
Publikováno v:
In Linear Algebra and Its Applications 15 May 2012 436(10):3819-3838
Akademický článek
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Autor:
Zander, Elmar
Spektrale stochastische Methoden haben sich als effizientes Werkzeug zur Modellierung von Systemen mit Unsicherheiten etabliert. Der Vorteil dieser Methoden ist, dass sie nicht nur Statistiken liefern, sondern auch eine direkte Darstellung der Lösun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92862d4e16420b2312c3bcac1280402b
http://publikationsserver.tu-braunschweig.de/get/64544
http://publikationsserver.tu-braunschweig.de/get/64544