000			03471nam a2200493 a 4500
001			000q0496
003			WSP
005			20260416153408.0
007			cr |nu|||unuuu
008			241109s2025 enk ob 001 0 eng d
010			_a2025000009
040			_aWSPC _beng _cWSPC
020			_a9781800616851 _q(ebook)
020			_a1800616856 _q(ebook)
020			_z9781800616844 _q(hbk.)
020			_z1800616848 _q(hbk.)
050	0	4	_aTA658.8
072		7	_aTEC _x063000 _2bisacsh
072		7	_aCOM _x004000 _2bisacsh
072		7	_aTEC _x002000 _2bisacsh
082	0	4	_a624.177130285 _223
049			_aMAIN
100	1		_aYoo, Kwangkyu Alex.
245	1	0	_aProbabilistic optimisation of composite structures : _bmachine learning for design optimisation / _cKwangkyu Alex Yoo, Omar Bacarreza, M H Ferri Aliabadi.
260			_aLondon : _bWorld Scientific Publishing Europe, _c©2025.
300			_a1 online resource (208 p.).
490	1		_aComputational and experimental methods in structures, _x2044-9283 ; _vvol. 15
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- 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.
520			_a"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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat reader.
650		0	_aStructural optimization _xMathematical models.
650		0	_aStructural optimization _xData processing.
650		0	_aComposite construction _xMathematical models.
650		0	_aComposite construction _xData processing.
650		0	_aMachine learning _xIndustrial applications.
655		0	_aElectronic books.
700	1		_aBacarreza, Omar.
700	1		_aAliabadi, M. H. _1https://id.oclc.org/worldcat/entity/E39PBJqqCGxrcG6hhQYdkrFMyd
830		0	_aComputational and experimental methods in structures.
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/q0496#t=toc
942			_cE-BOOK
999			_c49732 _d49732