000			02076 a2200217 4500
003			OSt
005			20250922150424.0
008			250917b |||||||| |||| 00| 0 eng d
020			_a9781800616844
082			_a629.7 _bY8p
100			_aYoo, Kwangkyu Alex
245			_aProbabilistic optimisation of composite structures _bmachine learning for design optimisation _cKwangkyu Alex Yoo, Omar Bacarreza and M. H. Ferri Aliabadi
260			_bWorld Scientific _c2025 _aLondon
300			_axi, 192p
440			_aComputational and experimental methods in structures
490			_a / edited by Ferri M. H. Aliabadi _v; v. 15
520			_aThis 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.
650			_aDesign optimisation _aMachine learning _aComposite structure in aircraft design
700			_aAliabadi, M. H. Ferri _aBacarreza, Omar
942			_cBK
999			_c567653 _d567653