A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations
type: Item
instance of: scholarly article
title: A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations
author: Jonas Nitzler
author: Jonas Biehler
author: Niklas Fehn
author: Phaedon-Stelios Koutsourelakis
author: Wolfgang A. Wall
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2022-10-10T00:00:00Z
full work available at URL: https://arxiv.org/abs/2001.02892
Mathematics Subject Classification ID: 76M35
Mathematics Subject Classification ID: 65C05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 76-10
zbMATH DE Number: 7598945
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: probabilistic learning
zbMATH Keywords: Bayes
zbMATH Keywords: multi-fidelity
zbMATH Keywords: fluid-structure interaction
zbMATH Keywords: small data
describes a project that uses: GPy
describes a project that uses: GitHub
MaRDI profile type: Publication
OpenAlex ID: W4296617977
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DOI: 10.1016/J.CMA.2022.115600
description: scientific article; zbMATH DE number 7598945