Probabilistic Optimisation Of Composite Structures: Machine Learning For Design Optimisation (Computational and Experimental Methods in Structures, Band 15) Nogales, Omar Bacarreza; Yoo, Kwangkyu Alex; Aliabadi, M H Ferri

Nogales, Omar Bacarreza; Yoo, Kwangkyu Alex; Aliabadi, M H Ferri

ISBN 10: 1800616848 / ISBN 13: 9781800616844

Published by World Scientific Publishing Company, 2025

About this Item

Probabilistic Optimisation Of Composite Structures: Machine Learning For Design Optimisation (Computational and Experimental Methods in Structures, Band 15) In deutscher Sprache. pages.

Seller Inventory # BN184593

Bibliographic Details

• Title: Probabilistic Optimisation Of Composite Structures: Machine Learning For Design Optimisation (Computational and Experimental Methods in Structures, Band 15) Nogales, Omar Bacarreza; Yoo, Kwangkyu Alex; Aliabadi, M H Ferri
• Publisher: World Scientific Publishing Company
• Publication Date: 2025
• Language: English
• ISBN 10: 1800616848
• ISBN 13: 9781800616844
• Binding: Hardcover
• Edition: 2025.
• Condition: gut
• Weight: 2000 grams

About this title

Synopsis

This book introduces an innovative approach to multi-fidelity probabilistic optimisation for aircraft composite structures, addressing the challenge of balancing reliability with computational cost. Probabilistic optimisation pursues statistically reliable and robust solutions by accounting for uncertainties in data, such as material properties and geometry tolerances. Traditional approaches using high-fidelity models, though accurate, are computationally expensive and time-consuming, especially when using complex methods such as Monte Carlo simulations and gradient calculations. For the first time, the proposed multi-fidelity method combines high- and low-fidelity models, enabling high-fidelity models to focus on specific areas of the design space, while low-fidelity models explore the entire space. Machine learning technologies, such as artificial neural networks and nonlinear autoregressive Gaussian processes, fill information gaps between different fidelity models, enhancing model accuracy. The multi-fidelity probabilistic optimisation framework is demonstrated through the reliability-based and robust design problems of aircraft composite structures under a thermo-mechanical environment, showing acceptable accuracy and reductions in computational time.