proFit: Probabilistic Response Model Fitting with Interactive Tools. This 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. proFit can be fed with a number of data points consisting of different input parameter combinations and the resulting output of the model under investigation. It then fits a response ”surface” through the point cloud. This probabilistic response model allows to predict (”interpolate”) the output at yet unexplored parameter combinations including uncertainty estimates. It can also tell you where to put more training points to gain maximum new information (experimental design) and automatically generate and start new simulation runs locally or on a cluster. Results can be explored and checked visually in a web frontend. Telling proFit how to interact with your existing simulations is easy and requires no changes in your existing code. Current functionality covers uncertainty quantification via polynomial chaos expansion with chaospy as a backend. Support for response surface / surrogate models via GPflow is under development. The web frontend is based on plotly/dash.
References in zbMATH (referenced in 1 article )
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- Rath, Katharina; Albert, Christopher G.; Bischl, Bernd; von Toussaint, Udo: Symplectic Gaussian process regression of maps in Hamiltonian systems (2021)