07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8

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Author: search.filters.author.Hose, Dominik

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Now showing 1 - 4 of 4
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    Possibilistic calculus as a conservative counterpart to probabilistic calculus
    (2019) Hose, Dominik; Hanss, Michael
    In this contribution, we revisit Zadeh's Extension Principle in the context of imprecise probabilities and present two simple modifications to obtain meaningful results when using possibilistic calculus to propagate credal sets of probability distributions through models. It is demonstrated how these results facilitate the possibilistic solution of two benchmark problems in uncertainty quantification.
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    On data-based estimation of possibility distributions
    (2019) Hose, Dominik; Hanss, Michael
    In this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a very general approach to possibilistic identification problems in the framework of imprecise probabilities, i.e. the non-parametric estimation of possibility distributions of uncertain variables from data with a clear interpretation.
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    On the solution of forward and inverse problems in possibilistic uncertainty quantification for dynamical systems
    (2020) Hose, Dominik; Hanss, Michael
    In this contribution, we adress an apparent lack of methods for the robust analysis of dynamical systems when neither a precise statistical nor an entirely epistemic description of the present uncertainties is possible. Relying on recent results of possibilistic calculus, we revisit standard prediction and filtering problems and show how these may be solved in a numerically exact way.
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    Analysis of mixed uncertainty through possibilistic inference by using error estimation of reduced order surrogate models
    (2022) Könecke, Tom; Hose, Dominik; Frie, Lennart; Hanss, Michael; Eberhard, Peter
    In the context of solving inverse problems, such as in statistical inference, an efficient repeated evaluability of a system can be achieved through methods of model order reduction. However, quantifying and adequately representing the emerging reduction error requires special techniques for combining different sources of uncertainty. In this paper, parametric finite element models are reduced through parametric model order reduction. The induced approximation error, an epistemic uncertainty, is reasonably estimated with the help of modern estimators for formulating statistical statements about the parameters to be identified. Measurement noise is also taken into account as a source of aleatory uncertainty. As a novel extension to analyzing a single source of uncertainty, the construction of a basic workflow for parameter identification in the face of both epistemic and aleatory uncertainties is presented, combining efficient error estimation techniques and possibilistic inference. The general applicability of this procedure is highlighted by two illustrative applications.