torchMDO
Multidisciplinary design optimization made fast with PyTorch and modern automatic differentiation.
At its heart, torchMDO is a library of optimizers and tools that allow you to build out large-scale models to assess a design in PyTorch (with its Numpy-like syntax) and to optimize the design extremely quickly by taking advantage of its automatic differentiation capabilities as well as its GPU acceleration.
Also, if you have a model that has previously been built in Python, you can convert it to PyTorch (which is typically straightforward if it was originally implemented in Numpy) and you can immediately plug it into torchMDO.
Examples
Aerodynamic shape optimization of a wing’s planform
In this simple example, we consider a 50-dimensional nonlinear constrained optimization problem to optimize the shape of a wing to minimize induced drag, subject to a wing-area equality constraint. We also compare the performance of modern automatic differentiation to the use of (classical) finite-difference methods.
Installation
Install using pip:
pip install torchmdo # minimal install
pip install torchmdo[examples] # to be able to run the examples
To upgrade to the latest (unstable) version, run:
pip install --upgrade git+https://github.com/treforevans/torchmdo.git
Documentation
- Online documentation:
- Source code repository (and issue tracker):
- License:
AGPL-3.0-or-later – please contact for inquiries about licensing.
Examples and Tutorials:
Package reference