Simulation-Driven Design by Knowledge-Based Response Correction Techniques

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Springer, May 13, 2016 - Mathematics - 262 pages
Focused on efficient simulation-driven multi-fidelity optimization techniques, this monograph on simulation-driven optimization covers simulations utilizing physics-based low-fidelity models, often based on coarse-discretization simulations or other types of simplified physics representations, such as analytical models. The methods presented in the book exploit as much as possible any knowledge about the system or device of interest embedded in the low-fidelity model with the purpose of reducing the computational overhead of the design process. Most of the techniques described in the book are of response correction type and can be split into parametric (usually based on analytical formulas) and non-parametric, i.e., not based on analytical formulas. The latter, while more complex in implementation, tend to be more efficient.

The book presents a general formulation of response correction techniques as well as a number of specific methods, including those based on correcting the low-fidelity model response (output space mapping, manifold mapping, adaptive response correction and shape-preserving response prediction), as well as on suitable modification of design specifications. Detailed formulations, application examples and the discussion of advantages and disadvantages of these techniques are also included. The book demonstrates the use of the discussed techniques for solving real-world engineering design problems, including applications in microwave engineering, antenna design, and aero/hydrodynamics.
 

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Contents

Introduction
1
SimulationDriven Design
7
Fundamentals of Numerical Optimization
15
Introduction to Surrogate Modeling and SurrogateBased Optimization
31
Design Optimization Using Response Correction Techniques
63
SurrogateBased Optimization Using Parametric Response Correction
75
Nonparametric Response Correction Techniques
99
Expedited SimulationDriven Optimization Using Adaptively Adjusted Design Specifications
131
SurrogateAssisted Design Optimization Using Response Features
147
Enhancing Response Correction Techniques by Adjoint Sensitivity
165
Multiobjective Optimization Using VariableFidelity Models and Response Correction
193
PhysicsBased Surrogate Modeling Using Response Correction
211
Summary and Discussion
244
References
249
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