Treffer: PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling.

PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models such as those used in the earth, environmental and engineering sciences. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent a wide range of methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of new or existing algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in groundwater. • Introduces a comprehensive easy-to-use toolbox for probabilistic analysis of models. • Automated methods for surrogate modeling, sensitivity analysis, uncertainty quantification. • Inference and experimental design methods reduce uncertainty in model inputs and predictions. • Multi-fidelity methods reduce computational cost when multiple models are available. • Provides extensive benchmark problems for validation and tutorials for learning. [ABSTRACT FROM AUTHOR]

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