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pro vyhledávání: '"Mike Eldred"'
Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, mul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::595bfb7e96eecc215d1319bb6c3bcdce
http://arxiv.org/abs/2007.03502
http://arxiv.org/abs/2007.03502
High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7d4c55dc2e05d82501c0d21535088dc
http://arxiv.org/abs/2003.09436
http://arxiv.org/abs/2003.09436
Publikováno v:
Structural Safety. 31:450-459
This paper presents a computational framework for structural design optimization under uncertainty. The stochastic static response of linear elastic structures is predicted by a spectral stochastic finite element method (SSFEM) based on a polynomial
Publikováno v:
Structural and Multidisciplinary Optimization. 38:599-611
Predicting the transient response of structures by high-fidelity simulation models within design optimization and uncertainty quantification often leads to unacceptable computational cost. This paper presents a reduced-order modeling (ROM) framework
Publikováno v:
Structural and Multidisciplinary Optimization. 27:97-109
A large-scale structural optimization of an electronics package has been completed using a massively parallel structural dynamics code. The optimization goals were to maximize safety margins for stress and acceleration resulting from transient impuls
Publikováno v:
47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 14th AIAA/ASME/AHS Adaptive Structures Conference 7th.
For large computational models, standard deterministic optimization approaches can be prohibitively expensive due to the need to repeatedly evaluate the model. This difficulty is amplified when stochastic aspects of the model are included, such as in