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Simulation-driven design by knowledg...

Koziel, Slawomir.

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Simulation-driven design by knowledge-based response correction techniques

Record Type:	Electronic resources : Monograph/item
Title/Author:	Simulation-driven design by knowledge-based response correction techniques/ by Slawomir Koziel, Leifur Leifsson.
Author:	Koziel, Slawomir.
other author:	Leifsson, Leifur.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xi, 262 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Simulation-Driven Design -- Fundamentals of Numerical Optimization -- Introduction to Surrogate-Based Modeling and Surrogate-Based Optimization -- Design Optimization Using Response Correction Techniques -- Surrogate-Based Optimization Using Parametric Response Correction -- Non-Parametric Response Correction Techniques -- Expedited Simulation-Driven Optimization Using Adaptively Adjusted Design Specification -- Surrogate-Assisted Design Optimization Using Response Features -- Enhancing Response Correction Techniques by Adjoint Sensitivity -- Multi-Objective Optimization Using Variable-Fidelity Models and Response Correction -- Physics-Base Surrogate Models Using Response Correction -- Summary and Discussion -- References.
Contained By:	Springer eBooks
Subject:	Mathematical optimization. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-30115-0
ISBN:	9783319301150

Simulation-driven design by knowledge-based response correction techniques
Koziel, Slawomir.

Simulation-driven design by knowledge-based response correction techniques[electronic resource] /by Slawomir Koziel, Leifur Leifsson. - Cham :Springer International Publishing :2016. - xi, 262 p. :ill. (some col.), digital ;24 cm.

Introduction -- Simulation-Driven Design -- Fundamentals of Numerical Optimization -- Introduction to Surrogate-Based Modeling and Surrogate-Based Optimization -- Design Optimization Using Response Correction Techniques -- Surrogate-Based Optimization Using Parametric Response Correction -- Non-Parametric Response Correction Techniques -- Expedited Simulation-Driven Optimization Using Adaptively Adjusted Design Specification -- Surrogate-Assisted Design Optimization Using Response Features -- Enhancing Response Correction Techniques by Adjoint Sensitivity -- Multi-Objective Optimization Using Variable-Fidelity Models and Response Correction -- Physics-Base Surrogate Models Using Response Correction -- Summary and Discussion -- References.

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.

ISBN: 9783319301150

Standard No.: 10.1007/978-3-319-30115-0doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Simulation-driven design by knowledge-based response correction techniques
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245 1 0 $a Simulation-driven design by knowledge-based response correction techniques $h [electronic resource] / $c by Slawomir Koziel, Leifur Leifsson.
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300 $a xi, 262 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Introduction -- Simulation-Driven Design -- Fundamentals of Numerical Optimization -- Introduction to Surrogate-Based Modeling and Surrogate-Based Optimization -- Design Optimization Using Response Correction Techniques -- Surrogate-Based Optimization Using Parametric Response Correction -- Non-Parametric Response Correction Techniques -- Expedited Simulation-Driven Optimization Using Adaptively Adjusted Design Specification -- Surrogate-Assisted Design Optimization Using Response Features -- Enhancing Response Correction Techniques by Adjoint Sensitivity -- Multi-Objective Optimization Using Variable-Fidelity Models and Response Correction -- Physics-Base Surrogate Models Using Response Correction -- Summary and Discussion -- References.
520 $a 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.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Computer simulation. $3 525602
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650 2 4 $a Discrete Optimization. $3 2054320
650 2 4 $a Continuous Optimization. $3 1566748
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
650 2 4 $a Computational Science and Engineering. $3 893018
700 1 $a Leifsson, Leifur. $3 2108898
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-30115-0
950 $a Mathematics and Statistics (Springer-11649)