Welcome to OpenMDAO¶
OpenMDAO is an open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python. It enables you to decompose your models, making them easier to build and maintain, while still solving them in a tightly coupled manner with efficient parallel numerical methods.
The OpenMDAO project is primarily focused on supporting gradient-based optimization with analytic derivatives to allow you to explore large design spaces with hundreds or thousands of design variables, but the framework also has a number of parallel computing features that can work with gradient-free optimization, mixed-integer nonlinear programming, and traditional design space exploration.
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These are a collection of tutorial problems that teach you important concepts and techniques for using OpenMDAO. For new users, you should work through all material in Getting Started and Basic User Guide. That represents the minimum set of information you need to understand to be able to work with OpenMDAO models.
You will also find tutorials in the Advanced User Guide to be very helpful as you grow more familiar with OpenMDAO, but you don’t need to read these right away. They explain important secondary concepts that you will run into when working with more complex OpenMDAO models.
These docs are intended to be used by as a reference by users looking for explanation of a particular feature in detail or documentation of the arguments/options/settings for a specific method, Component, Driver, or Solver.