simsopt 0.2.3

Framework for optimizing stellarators

simsopt

simsopt is a framework for optimizing stellarators. The high-level routines of simsopt are in python, with calls to C++ or fortran where needed for performance. Several types of components are included:

• Interfaces to physics codes, e.g. for MHD equilibrium.
• Tools for defining objective functions and parameter spaces for optimization.
• Geometric objects that are important for stellarators - surfaces and curves - with several available parameterizations.
• An efficient implementation of the Biot-Savart law, including derivatives.
• Tools for parallelized finite-difference gradient calculations.

Some of the physics modules with compiled code reside in separate repositories. These separate modules include

• VMEC, for MHD equilibrium.
• SPEC, for MHD equilibrium. (This repository is private.)
• booz_xform, for Boozer coordinates and quasisymmetry.

The design of simsopt is guided by several principles:

• Thorough unit testing, regression testing, and continuous integration.
• Extensibility: It should be possible to add new codes and terms to the objective function without editing modules that already work, i.e. the open-closed principle. This is because any edits to working code can potentially introduce bugs.
• Modularity: Physics modules that are not needed for your optimization problem do not need to be installed. For instance, to optimize SPEC equilibria, the VMEC module need not be installed.
• Flexibility: The components used to define an objective function can be re-used for applications other than standard optimization. For instance, a simsopt objective function is a standard python function that can be plotted, passed to optimization packages outside of simsopt, etc.

We gratefully acknowledge funding from the Simons Foundation's Hidden symmetries and fusion energy project. simsopt is fully open-source, and anyone is welcome to make suggestions, contribute, and use.

simsopt is one of several available systems for stellarator optimization. Others include STELLOPT, ROSE, and LASSO.

Several methods are available for installing simsopt. One recommended approach is to first clone the repository using

git clone https://github.com/hiddenSymmetries/simsopt.git

Then install the package to your local python environment with

cd simsopt
pip install -e .

For more installation options and other information, see the main simsopt documentation here.