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

Jakeman, John Davis

doi:10.1016/j.envsoft.2023.105825

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

Journal Article · 03 October 2023 · Environmental Modelling and Software

DOI: https://doi.org/10.1016/j.envsoft.2023.105825 · OSTI ID:2311280

^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

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. Here, 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.

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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); Defense Advanced Research Projects Agency (DARPA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311280

Report Number(s):: SAND--2023-13522J

Journal Information:: Environmental Modelling and Software, Journal Name: Environmental Modelling and Software Journal Issue: 1 Vol. 170; ISSN 1364-8152

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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54 ENVIRONMENTAL SCIENCES
97 MATHEMATICS AND COMPUTING
Bayesian inference
Decision making
Experimental design
Modeling
Multi-fidelity
Sensitivity analysis
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Uncertainty quantification

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

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