A problem of probability density function estimation for large dimensional spaces with many low-influential dimensions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU147270" target="_blank" >RIV/00216305:26210/21:PU147270 - isvavai.cz</a>

Result on the web

<a href="https://www.scipedia.com/public/Suja_et_al_2021a" target="_blank" >https://www.scipedia.com/public/Suja_et_al_2021a</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23967/wccm-eccomas.2020.037" target="_blank" >10.23967/wccm-eccomas.2020.037</a>

Result language

angličtina

Original language name

A problem of probability density function estimation for large dimensional spaces with many low-influential dimensions

Original language description

All engineering problems consider uncertainties. These range from small production uncertainties to large-scale uncertainties coming from outside, such as variable wind speed or sunlight. Currently, modern methods for uncertainty propagation have large difficulties with estimation of statistics for large-scale problems which considers hundreds of these uncertain parameters. Due to the complexity of the problem and limitations of the modern methods, a common approach for modelling large scale problems is to select a few important parameters and model statistics for these parameters. However, this can lead to an important problem. In this paper, an application of the UptimAI’s UQ propagation algorithm is used to discuss a new problem arising from very high dimensional spaces where a large number of parameters have negligible impact on the final solution. In other words, when a problem consists of a great number of uncertain design parameters, common practice is to focus on the most important ones and neglect the non-influential ones. However, a combination of a great number of noninfluential parameters can lead to completely different results. This is especially a problem for modelling large dimensional statistical models where a common approach is to perform sensitivity analysis and neglect the non-influential variables, i.e. set the non-influential variables to nominal value. Therefore, using a common approach of neglecting the non-influential variables could lead to a dramatic error and hence, we call this problem ”many times nothing killed a horse”. This problem cannot be observed for cases with a small number of design parameters, which are commonly solved in statistical modelling. The reason for this issue is that the combined influence of neglected variables is extremely small and such that has no influence on the final output. Application of the UptimAl’s UQ propagation algorithm to modern engineering problems and the possibilities of mitigation of the cumulat

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

14th WCCM-ECCOMAS Congress 2020

ISBN

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ISSN

2696-6999

e-ISSN

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Number of pages

12

Pages from-to

1-12

Publisher name

scipedia

Place of publication

neuveden

Event location

Paris

Event date

Jan 11, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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