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Engineering Design Optimization

Authors

Joaquim R. R. A. Martins
, University of Michigan, Ann Arbor,
Andrew Ning
, Brigham Young University, Utah
Published 2021

Description

Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and…

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Key features

  • Hands on and applied applications related to aerospace, civil, mechanical, electrical, and chemical engineering.
  • Multidisciplinary approach.
  • Discusses the OpenMDAO framework an open-source high-performance computing platform for efficient optimization.
  • Covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty.
  • Includes over 400 high-quality visualizations and numerous examples.
  • Provides numerous end-of-chapter homework problems, progressing from easier problems through to open-ended problems, with a solutions manual online for instructors.

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