Profit is a collection of tools for studying parametric dependencies of black-box simulation codes or experiments and construction of reduced order response models over input parameter space. For regression it supports Gaussian process models as well as polynomial chaos expansion, allowing the construction of response surface / surrogate models with uncertainty quantification and sensitivity analysis. For this purpose, custom backends are available as well as interfaces to the generic libraries GPflow and chaospy. Run configurations of simulation codes can be auto-generated based on templates and started locally or on HPC clusters. Web-enabled visualization of results is performed via Plotly/Dash.

{"references": ["Matthews, Alexander G. de G. et al. (2017). GPflow: A Gaussian process library using TensorFlow. J. Mach. Learn. 18.1, 1299-1304", "Feinberg, Jonathan, and Langtangen, Hans Petter (2015). Chaospy: An open source tool for designing methods of uncertainty quantification. J, Comp. Sci. 11, 46-57", "Perkel, Jeffrey M. (2018). Data visualization tools drive interactivity and reproducibility in online publishing. Nature 554, 133-134"]}

This study is a contribution to the Reduced Complexity Models grant number ZT-I-0010 funded by the Helmholtz Association of German Research Centers.