Reliability analysis of performance functions via adaptive sequential sampling with detection of failure surfaces

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F26%3A0200361" target="_blank" >RIV/00216305:26110/26:0200361 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scipedia.com/public/Vorechovsky_2025a" target="_blank" >https://www.scipedia.com/public/Vorechovsky_2025a</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23967/icossar.2025.094" target="_blank" >10.23967/icossar.2025.094</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliability analysis of performance functions via adaptive sequential sampling with detection of failure surfaces

Popis výsledku v původním jazyce

We propose an improved method for estimating rare event probabilities in computational models with smooth performance functions. Building on a previously developed robust strategy for generally non-smooth or discrete-state performance functions, we enhance scalability and efficiency by replacing the original nearest-neighbor surrogate with a Gaussian process regression model. This surrogate leverages numerical limit state values to preselect potential candidates in an active learning scheme that balances exploration and exploitation. The resulting method significantly reduces the number of required evaluations, particularly in low-dimensional problems, while extending applicability to higher-dimensional settings.

Název v anglickém jazyce

Reliability analysis of performance functions via adaptive sequential sampling with detection of failure surfaces

Popis výsledku anglicky

We propose an improved method for estimating rare event probabilities in computational models with smooth performance functions. Building on a previously developed robust strategy for generally non-smooth or discrete-state performance functions, we enhance scalability and efficiency by replacing the original nearest-neighbor surrogate with a Gaussian process regression model. This surrogate leverages numerical limit state values to preselect potential candidates in an active learning scheme that balances exploration and exploitation. The resulting method significantly reduces the number of required evaluations, particularly in low-dimensional problems, while extending applicability to higher-dimensional settings.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/LUAUS24260" target="_blank" >LUAUS24260: Rozvoj polynomiálního chaosu s fyzikálním omezením pro stochastickou mechaniku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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