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Data-Driven Uncertainty Quantification for Large-Scale Simulations

Data-Driven Uncertainty Quantification for Large-Scale Simulations

Fabian Franzelin

 

84,00 EUR
Nicht lieferbar



84,00 EUR
Nicht lieferbar



Produktinformation


Übersicht


Verlag : Dr. Hut
Buchreihe : Informatik
Sprache : Englisch
Erschienen : 15. 08. 2018
Seiten : 175
Einband : Gebunden
Höhe : 240 mm
Breite : 170 mm
Gewicht : 482 g
ISBN : 9783843936965
Sprache : Englisch

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Produktinformation


The computational demand of single large-scale simulation runs poses limits to the quantification of uncertainties (UQ), especially with non-intrusive methods that aim to approximate the response surface for the problem at hand. Numerical approximations suffer from the curse of dimensionality, the exponential dependency of the computational effort on the number of input parameters. To reduce the computational demand, an adaptive representation and exploration of such response surfaces is required. Furthermore, the uncertainty in the input affects heavily the uncertainty of the model's output, hence, reliable data-driven modeling of the input's uncertainty is essential. However, such inputs are rarely mutually independent, which decreases the efficiency of non-intrusive surrogates.

In this thesis, we propose to consider adaptive sparse grids to for data-driven uncertainty quantification for large-scale simulations. First, we introduce a new density estimation method based on extended sparse grids to describe the probability density of random input parameters. Furthermore, we provide algorithms to compute expectation values, covariances, marginal and conditional densities, which are required to define probabilistic transformations such as the Rosenblatt and the Nataf transformation. Second, we introduce different variants of sparse grids including new refinement criteria to propagate uncertainties for simulations that encounter non-smooth parameter dependencies or even shock phenomena. For smooth parameter dependencies we present Leja sequences as adaptive data-driven sampling scheme for arbitrary polynomial chaos expansion. Third, we combine the input modeling methods and the surrogate modeling techniques to solve inverse problems with Bayes' theorem.

We demonstrate the advantages of the newly introduced techniques on the basis of various engineering problems from crack propagation simulations in the context of material sciences to measuring leakage of carbon dioxide at a fault in a cap rock for subsurface flows.

Deine Buchhandlung


Buchhandlung LeseLust
Inh. Gernod Siering

Georgenstraße 2
99817 Eisenach

03691/733822
kontakt@leselust-eisenach.de

Montag-Freitag 9-17 Uhr
Sonnabend 10-14 Uhr



Deine Buchhandlung
Buchhandlung LeseLust
Inh. Gernod Siering

Georgenstraße 2
99817 Eisenach

03691/733822
kontakt@leselust-eisenach.de

Montag-Freitag 9-17 Uhr
Sonnabend 10-14 Uhr