scholarly journals EXPERIENCING THE CONCEPTUAL WEALTH OF NON‐DERIVED DENOMINAL VERBS: A MULTI‐LEVEL, SIMULATION‐BASED APPROACH *

2021 ◽  
Author(s):  
Heike Baeskow
Keyword(s):  
Simulation Based ◽  
Multi Level

scholarly journals Simulation-Based Virtual Cycle for Multi-Level Airport Analysis

Aerospace ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 44
Author(s):  
Miguel Mota ◽  
Alejandro Di Bernardi ◽  
Paolo Scala ◽  
Gabriel Ramirez-Diaz
Keyword(s):  
Simulation Based ◽  
Multi Level

scholarly journals sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Anh Tran ◽  
Tim Wildey ◽  
Scott McCann
Keyword(s):  
Thermal Loading ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Binary Classifier ◽  
Predictive Capability ◽  
Optimization Framework ◽  
Simulation Based ◽  
Multi Level ◽  
Multiple Levels

Abstract Bayesian optimization (BO) is an efiective surrogate-based method that has been widely used to optimize simulation-based applications. While the traditional Bayesian optimization approach only applies to single-fidelity models, many realistic applications provide multiple levels of fidelity with various computational complexity and predictive capability. In this work, we propose a multi-fidelity Bayesian optimization method for design applications with both known and unknown constraints. The proposed framework, called sMF-BO-2CoGP, is built on a multi-level CoKriging method to predict the objective function. An external binary classifier, which we approximate using a separate CoKriging model, is used to distinguish between feasible and infeasible regions. The sMF-BO-2CoGP method is demonstrated using a series of analytical examples, and a fiip-chip application for design optimization to minimize the deformation due to warping under thermal loading conditions.

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