OpenMDAO/OpenMDAO

What it solves

OpenMDAO is designed for systems analysis and multidisciplinary optimization. It addresses the challenge of managing complex models that need to be solved in a tightly coupled manner while remaining easy to build and maintain.

How it works

The platform uses Python to allow users to decompose their models into smaller, manageable components. It employs efficient parallel numerical methods to solve these models, with a primary focus on on gradient-based optimization using analytic derivatives. It also supports gradient-free optimization, mixed-integer nonlinear programming, and traditional design space exploration.

Who it’s for

It is intended for researchers and engineers who need to explore large design spaces with hundreds or thousands of design variables across multiple disciplines.

Highlights

• High-performance computing platform written in Python.
• Supports gradient-based optimization with analytic derivatives for large design spaces.
• Parallel computing features for gradient-free and mixed-integer nonlinear programming.
• Integration with third-party dependencies like numpy, scipy, and mpi4py for high-performance numerical work.