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Books > Science & Mathematics > Mathematics > Optimization
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Simulation-Driven Design by Knowledge-Based Response Correction Techniques (Hardcover, 1st ed. 2016)
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Simulation-Driven Design by Knowledge-Based Response Correction Techniques (Hardcover, 1st ed. 2016)
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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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