Results 261 to 270 of about 223,298 (311)
Sparse polynomial surrogates for F-actin networks with compliant crosslinkers. [PDF]
Biomech Model MechanobiolPacheco L, Parente M, Ferreira J.
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Query-Efficient Hard-Label Attack: A Prior-Guided Adam Ray Search Optimization. [PDF]
Sensors (Basel)Ding T, Xu X, Xuan Q, Yu H, Ma C.
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Surrogate-Based EM Design of RF and Microwave Components: A Systematic Review of Workflow Roles, Inverse Design, Multifidelity, and Active Learning. [PDF]
Sensors (Basel)Prousali M, Tsitsos S.
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Automatic selection for general surrogate models
Structural and Multidisciplinary Optimization, 2018 International audienceIn design engineering problems, the use of surro-gate models (also called metamodels) instead of expensive simulations have become very popular. Surrogate models include individual models (regression, kriging, neural network ...) or
Malek Ben Salem
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Protecting SLAs with surrogate models
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems, 2010In this paper, we propose the use of surrogate models to avoid or limit violations of the service level agreements (protect SLAs) of enterprise applications executed within virtualized data centers (VDCs).Modern enterprise services are delivered along with service level agreements (SLAs) that formalize the expected quality of service, and define ...
Alessio Gambi +2 more
openaire +2 more sources
2019
This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
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This Chapter presents the first key component of BO, that is, the probabilistic surrogate model. Section 3.1 is focused on Gaussian processes (GPs); Sect. 3.2 introduces the sequential optimization method known as Thompson sampling, also based on GP; finally, Sect. 3.3 presents other probabilistic models which might represent, in some cases, a suitable
Francesco Archetti, Antonio Candelieri
openaire +1 more source