Results 181 to 190 of about 9,630 (234)

Accurate modeling of microwave structures in constrained domains using global sensitivity analysis and performance-based pre-screening. [PDF]

Sci Rep
Koziel S, Pietrenko-Dabrowska A.
europepmc   +1 more source

Design optimization of multiple cooling plate battery thermal management system for minimizing temperature difference considering interactions between cooling plates. [PDF]

Sci Rep
Lee H, Park S, Park C, Kim CW.
europepmc   +1 more source

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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Surrogate modeling for time-dependent reliability analysis of robotic manipulator trajectories. [PDF]

PLoS One
Ayoob K, Elahi H, Zafar T, Hamza A, Wang Z.   +4 more
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Multi-Objective Optimization of Dental Implant Designs With Multi-Recessed Holes: Insights From Static and Dynamic Finite Element Analysis. [PDF]

Int J Numer Method Biomed Eng
Chou IC, Huang SH, Lee CK, Prajapati MJ, Huang JC, Cheng YC, Jiang CP.   +6 more
europepmc   +1 more source

Hull form optimization with a new three-dimensional deformation strategy. [PDF]

Sci Rep
Wei D, Wang L, Yang J, Sun X, Ji X, Liu X.   +5 more
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Sparse Reconstruction of Pressure Field for Wedge Passive Fluidic Thrust Vectoring Nozzle. [PDF]

Sensors (Basel)
Huang Z, Gu Y, Xu Q, Li L.
europepmc   +1 more source

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Nested Kriging Modeling

2020
This chapter discusses an alternative approach to performance-driven surrogate modeling, referred to as nested kriging. The technique involves construction of two levels of surrogates. The first-level model maps the objective space into the geometry parameter space of the system in order to establish the surrogate model domain. The domain is defined as
Slawomir Koziel, Anna Pietrenko-Dabrowska   +1 more
openaire   +1 more source

Robust Kriging models in computer experiments

Journal of the Operational Research Society, 2016
In the Gaussian Kriging model, errors are assumed to follow a Gaussian process. This is reasonable in many cases, but such an assumption is not appropriate for the situations when outliers are present. Large prediction errors may occur in those cases and more robust estimation is critical.
Taejin Park, Bong-Jin Yum, Ying Hung, Young-Seon Jeong 0001, Myong K. Jeong   +4 more
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