MDO Lab Reading Guide

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This is a reading guide for engineering design optimization theory and applications. The guide is organized by topic. For some topics, there numbered levels. You can start with any topic and go to any level within that topic. However, we recommend everyone have achieved at least Level 1 of the fundamentals. The guide is tailored to MDO Lab members, so it is biased toward our publications and the topics we research.

Overviews of MDO Lab Research

Optimization Fundamentals

Beginner

Intermediate

Advanced

Multidisciplinary Design Optimization (MDO)

MDO Architectures Fundamentals

OpenMDAO

OpenMDAO is a framework that facilitates the coupling of different models, their coupled solution, and computation of coupled derivatives (to use with gradient-based optimization). OpenMDAO is not an optimizer (although it uses optimizers, either directly or through pyOptSparse). Also, do not confuse the models built using OpenMDAO as being part of OpenMDAO. OpenMDAO is just the framework.

There are different levels of understanding of OpenMDAO. Level 1 is the minimum required to use OpenMDAO. Level 2 is required for building components in OpenMDAO.

Beginner

Intermediate

References

  1. Engineering Design Optimization

    J. R. R. A. Martins, and A. Ning

    2022

    doi:10.1017/9781108980647

    Details
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  3. Enabling Large-scale Multidisciplinary Design Optimization through Adjoint Sensitivity Analysis

    J. R. R. A. Martins, and G. J. Kennedy

    Structural and Multidisciplinary Optimization, 642959–2974, 2021

    doi:10.1007/s00158-021-03067-y

    Details
  4. pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems

    N. Wu, G. Kenway, C. A. Mader, J. Jasa, and J. R. R. A. Martins

    Journal of Open Source Software, 5(54):2564, 2020

    doi:10.21105/joss.02564

    Details
  5. OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization

    J. S. Gray, J. T. Hwang, J. R. R. A. Martins, K. T. Moore, and B. A. Naylor

    Structural and Multidisciplinary Optimization, 59(4):1075–1104, 2019

    doi:10.1007/s00158-019-02211-z

    Details
  6. A computational architecture for coupling heterogeneous numerical models and computing coupled derivatives

    J. T. Hwang, and J. R. R. A. Martins

    ACM Transactions on Mathematical Software, 44(4):Article 37, 2018

    doi:10.1145/3182393

    Details
  7. Multidisciplinary Design Optimization: A Survey of Architectures

    J. R. R. A. Martins, and A. B. Lambe

    AIAA Journal, 51(9):2049–2075, 2013

    doi:10.2514/1.J051895

    Details
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