Probabilistic optimisation of composite structures : machine learning for design optimisation /
Yoo, Kwangkyu Alex.
Probabilistic optimisation of composite structures : machine learning for design optimisation / Kwangkyu Alex Yoo, Omar Bacarreza, M H Ferri Aliabadi. - London : World Scientific Publishing Europe, ©2025. - 1 online resource (208 p.). - Computational and experimental methods in structures, vol. 15 2044-9283 ; . - Computational and experimental methods in structures. .
Includes bibliographical references and index.
Introduction -- Fundamentals of structural optimisation -- Multi-fidelity models -- Multi-fidelity reliability-based design optimisation -- Multi-fidelity robust design optimisation using successive high-fidelity correction -- Multi-fidelity probabilistic optimisation using sparse high-fidelity information - Conclusion.
"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"--
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
9781800616851 1800616856
2025000009
Structural optimization--Mathematical models.
Structural optimization--Data processing.
Composite construction--Mathematical models.
Composite construction--Data processing.
Machine learning--Industrial applications.
Electronic books.
TA658.8
624.177130285
Probabilistic optimisation of composite structures : machine learning for design optimisation / Kwangkyu Alex Yoo, Omar Bacarreza, M H Ferri Aliabadi. - London : World Scientific Publishing Europe, ©2025. - 1 online resource (208 p.). - Computational and experimental methods in structures, vol. 15 2044-9283 ; . - Computational and experimental methods in structures. .
Includes bibliographical references and index.
Introduction -- Fundamentals of structural optimisation -- Multi-fidelity models -- Multi-fidelity reliability-based design optimisation -- Multi-fidelity robust design optimisation using successive high-fidelity correction -- Multi-fidelity probabilistic optimisation using sparse high-fidelity information - Conclusion.
"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"--
Mode of access: World Wide Web.
System requirements: Adobe Acrobat reader.
9781800616851 1800616856
2025000009
Structural optimization--Mathematical models.
Structural optimization--Data processing.
Composite construction--Mathematical models.
Composite construction--Data processing.
Machine learning--Industrial applications.
Electronic books.
TA658.8
624.177130285