PUQ; A code for non-intrusive uncertainty propagation in computer simulations

We present a software package for the non-intrusive propagation of uncertainties in input parameters through computer simulation codes or mathematical models and associated analysis; we demonstrate its use to drive micromechanical simulations using a phase field approach to dislocation dynamics. The PRISM uncertainty quantification framework (PUQ) offers several methods to sample the distribution of input variables and to obtain surrogate models (or response functions) that relate the uncertain inputs with the quantities of interest (QoIs); the surrogate models are ultimately used to propagate uncertainties. PUQ requires minimal changes in the simulation code, just those required to annotate the QoI(s) for its analysis. Collocation methods include Monte Carlo, Latin Hypercube and Smolyak sparse grids and surrogate models can be obtained in terms of radial basis functions and via generalized polynomial chaos. PUQ uses the method of elementary effects for sensitivity analysis in Smolyak runs. The code is available for download and also available for cloud computing in nanoHUB. PUQ orchestrates runs of the nanoPLASTICITY tool at nanoHUB where users can propagate uncertainties in dislocation dynamics simulations using simply a web browser, without downloading or installing any software. Program summary Program title: PUQ Catalogue identifier: AEWP_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEWP_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT license No. of lines in distributed program, including test data, etc.: 45075 No. of bytes in distributed program, including test data, etc.: 3318862 Distribution format: tar.gz Programming language: Python, C. Computer: Workstations. Operating system: Linux, Mac OSX. Classification: 4.11, 4.12, 4.13, External routines: SciPy, Matplotlib, h5py Nature of problem: Uncertainty propagation and creation of response surfaces. Solution method: Generalized Polynomial Chaos (gPC) using Smolyak sparse grids. Running time: PUQ performs uncertainty quantification and sensitivity analysis by running a simulation multiple times using different values for input parameters. Its run time will be the product of the run time of the chosen simulation code and the number of runs required to achieve the desired accuracy. (C) 2015 Elsevier B.V. All rights reserved.