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A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses
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“Aerodynamic efficiency improvements need turbulence modeling that is often computationally intensive and relies heavily on ML-based surrogate models [865].”
From Tackling climate change with machine learning (2023) · cites this paper as [865]
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“As shown in Table 2, commonly-used surrogate models include the polynomial response surface method, kriging, and neural networks [343, 344].”
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Metadata
- Authors
- Yondo, R., Andrés, E., & Valero, E.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Ingeniería de Sistemas para la Defensa de España (ES)
- Corresponding author
- Yondo, Raul
- Venue
- Progress in Aerospace Sciences
- Publisher
- Elsevier BV
- Published
- 2018-01
- Volume
- 96
- Pages
- 23-61
- ISSN
- 0376-0421 (ISSN-L)
- OpenAlex
- W2776493547
- Cited by
- 311
- References
- 548
- Citations by year
- 2026: 29 · 2025: 50 · 2024: 44 · 2023: 32 · 2022: 49 · 2021: 41 · 2020: 39 · 2019: 26 · 2018: 1
- FWCI
- 13.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Progress in Aerospace Sciences (publishedVersion)
- Funders
- European Commission Seventh Framework Programme (FP7-PEOPLE-2013-ITN, 608087)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Engineering › Computational Mechanics › Probabilistic and Robust Engineering Design
- Physical Sciences › Engineering › Computational Mechanics › Computational Fluid Dynamics and Aerodynamics
- Keywords
- surrogate models, aircraft aerodynamic design, surrogate-based optimization, experimental design, sampling strategy, dimensionality reduction, sensitivity analysis, uncertainty quantification, multi-fidelity modeling, civil transport aircraft, computational complexity reduction
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