Dakotathon
The CSDMS Dakota Interface
Dakota is a software toolkit, developed at Sandia National Laboratories, that provides an interface between models and a library of analysis methods, including support for sensitivity analysis, uncertainty quantification, optimization, and calibration techniques.
The CSDMS Dakota Interface, or Dakotathon, is a Python package that wraps and extends Dakota’s file-based user interface. It simplifies the process of configuring and running a Dakota experiment, and it allows a Dakota experiment to be scripted. Any model written in Python that exposes a Basic Model Interface (BMI), as well as any model componentized in the CSDMS modeling framework, automatically works with Dakotathon.
Currently, six Dakota analysis methods have been implemented from the much larger Dakota library:
- vector parameter study,
- centered parameter study,
- multidim parameter study,
- sampling,
- polynomial chaos, and
- stochastic collocation.
Additional information:
- The CSDMS Model page for Dakotathon
