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Numerical PDE-Constrained Optimization: A Comprehensive Guide for Engineers and Scientists

Kevin Turner

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Published in Numerical PDE Constrained Optimization (SpringerBriefs In Optimization)

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Numerical PDE-constrained optimization (PDE-CO) is a powerful technique for solving complex engineering and scientific problems. It combines the principles of numerical optimization with the governing equations of physics and engineering to find optimal solutions for a wide range of applications.

In this book, we provide a comprehensive overview of numerical PDE-CO, with a focus on the practical implementation of algorithms and software for real-world applications. We cover the fundamental concepts of PDE-CO, including problem formulation, discretization methods, optimization algorithms, and software implementation. We also provide numerous examples and case studies to illustrate the application of PDE-CO to a variety of engineering and scientific problems.

Numerical PDE-CO offers a number of advantages over traditional optimization methods:

Numerical PDE-Constrained Optimization (SpringerBriefs in Optimization)

by Juan Carlos De los Reyes

4.2 out of 5

Language	:	English
File size	:	3183 KB
Print length	:	133 pages
Screen Reader	:	Supported
X-Ray for textbooks	:	Enabled

Accuracy: Numerical PDE-CO can achieve high levels of accuracy by solving the governing equations of physics and engineering directly.
Robustness: Numerical PDE-CO is robust to noise and uncertainty in the input data.
Efficiency: Numerical PDE-CO can be efficient for solving large-scale problems.
Versatility: Numerical PDE-CO can be applied to a wide range of engineering and scientific problems.

Numerical PDE-CO has been successfully applied to a variety of engineering and scientific problems, including:

Optimal design of fluid systems: Numerical PDE-CO can be used to design fluid systems that maximize performance and minimize cost.
Optimal control of chemical processes: Numerical PDE-CO can be used to control chemical processes to maximize yield and minimize waste.
Structural optimization: Numerical PDE-CO can be used to optimize the design of structures to maximize strength and minimize weight.
Electromagnetic optimization: Numerical PDE-CO can be used to optimize the design of electromagnetic devices to maximize efficiency and minimize interference.

The book is divided into three parts:

Part I: Fundamentals of Numerical PDE-CO

This part provides an overview of the fundamental concepts of numerical PDE-CO, including:

Problem formulation
Discretization methods
Optimization algorithms
Software implementation

Part II: Applications of Numerical PDE-CO

This part provides a number of examples and case studies that illustrate the application of numerical PDE-CO to a variety of engineering and scientific problems, including:

Optimal design of fluid systems
Optimal control of chemical processes
Structural optimization
Electromagnetic optimization

Part III: Advanced Topics in Numerical PDE-CO

This part covers advanced topics in numerical PDE-CO, including:

Parallel computing
Uncertainty quantification
Robust optimization

This book is intended for engineers and scientists who are interested in using numerical PDE-CO to solve complex engineering and scientific problems. The book assumes a basic understanding of numerical optimization and the governing equations of physics and engineering.

The authors of this book are leading experts in the field of numerical PDE-CO. They have a wealth of experience in developing and applying numerical PDE-CO algorithms to a variety of engineering and scientific problems.

Numerical PDE-CO is a powerful technique for solving complex engineering and scientific problems. This book provides a comprehensive overview of numerical PDE-CO, with a focus on the practical implementation of algorithms and software for real-world applications. We hope that this book will help engineers and scientists to use numerical PDE-CO to solve their most challenging problems.

Numerical PDE-Constrained Optimization (SpringerBriefs in Optimization)

by Juan Carlos De los Reyes

4.2 out of 5