Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Garbo, Andrea and Parekh, Jigar and Rischmann, Tilo and Bekemeyer, Philipp (2024) Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification. Aerospace, 11 (6). Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/aerospace11060448. ISSN 2226-4310.

PDF - Published version
2MB

Official URL: https://www.mdpi.com/2226-4310/11/6/448

Item URL in elib:

https://elib.dlr.de/206001/

Document Type:

Article

Title:

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Garbo, Andrea	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Parekh, Jigar	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Rischmann, Tilo	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Bekemeyer, Philipp	UNSPECIFIED	https://orcid.org/0009-0001-9888-2499	UNSPECIFIED

Date:

31 May 2024

Journal or Publication Title:

Aerospace

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

Yes

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

DOI:

10.3390/aerospace11060448

Publisher:

Multidisciplinary Digital Publishing Institute (MDPI)

ISSN:

2226-4310

Status:

Published

Keywords:

Multi-Fidelity, Optimization, Uncertainty Quantification, Aerodynamics, CFD

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Aeronautics

HGF - Program Themes:

Efficient Vehicle

DLR - Research area:

Aeronautics

DLR - Program:

L EV - Efficient Vehicle

DLR - Research theme (Project):

L - Digital Technologies

Location:

Braunschweig

Institutes and Institutions:

Institute for Aerodynamics and Flow Technology > CASE, BS

Deposited By:

Bekemeyer, Philipp

Deposited On:

23 Oct 2024 10:01

Last Modified:

02 Dec 2025 13:24

Repository Staff Only: item control page