tag:joss.theoj.org,2005:/papers/tagged/uncertaintyJournal of Open Source Software2024-03-05T15:11:10ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/48622024-03-05T15:11:10Z2024-03-06T16:03:15ZPDOPT: A Python library for Probabilistic Design space exploration and OPTimisationacceptedv0.452023-10-23 15:08:48 UTC952024-03-05 15:11:10 UTC920246110AndreaSpinelliCentre for Propulsion and Thermal Power, Cranfield University, MK430AL, UK0000-0002-3064-3387TimoleonKipourosCentre for Propulsion and Thermal Power, Cranfield University, MK430AL, UK0000-0003-3392-283X10.21105/joss.06110https://doi.org/10.5281/zenodo.10732017Pythonhttps://joss.theoj.org/papers/10.21105/joss.06110.pdfComputational Engineering, Design Space Exploration, Set-Based Design, Design Uncertainty, Multi-Objective Optimizationtag:joss.theoj.org,2005:Paper/45742023-12-18T18:08:54Z2023-12-19T00:00:30Zparafields: A generator for distributed, stationary Gaussian processesacceptedv0.3.02023-07-03 12:18:52 UTC922023-12-18 18:08:54 UTC820235735DominicKempfScientific Software Center, Heidelberg University, Heidelberg, Germany, Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany0000-0002-6140-2332OleKleinIndependent Researcher, Heidelberg, Germany0000-0002-3295-7347RobertKutriInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0009-0004-8123-4673RobertScheichlInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany, Institute for Mathematics, Heidelberg University, Heidelberg, Germany0000-0001-8493-4393PeterBastianInterdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany10.21105/joss.05735https://doi.org/10.5281/zenodo.10355636Python, C++, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05735.pdfMPI, scientific computing, high performance computing, uncertainty quantification, random field generation, circulant embeddingtag:joss.theoj.org,2005:Paper/45872023-10-30T21:01:26Z2023-10-31T20:12:32ZUQTestFuns: A Python3 library of uncertainty quantification (UQ) test functionsacceptedv0.4.02023-07-07 17:24:02 UTC902023-10-30 21:01:26 UTC820235671DamarWicaksonoCenter for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany0000-0001-8587-7730MichaelHechtCenter for Advanced Systems Understanding (CASUS) - Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Germany0000-0001-9214-825310.21105/joss.05671https://doi.org/10.5281/zenodo.10047512Pythonhttps://joss.theoj.org/papers/10.21105/joss.05671.pdftest functions, benchmark, uncertainty quantification, metamodeling, surrogate modeling, sensitivity analysis, reliability analysis, rare event estimationtag:joss.theoj.org,2005:Paper/33022023-10-27T15:31:05Z2023-10-28T00:00:41ZUncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelinesacceptedv0.2.0b2022-01-21 23:18:23 UTC902023-10-27 15:31:05 UTC820234249JessTateScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA0000-0002-2934-1453ZexinLiuScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Mathematics Department, University of Utah, Salt Lake City, UT, USA0000-0003-3409-5709JakeA.BergquistScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA0000-0002-4586-6911SumientraRampersadPhysics Department, University of Massachusetts, Boston, MA, USA, Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USA0000-0001-9860-4459DanWhiteScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USAChantelCharleboisScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA0000-0002-4139-3539LindsayRuppScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA0000-0002-2688-7688DanaH.BrooksElectrical and Computer Engineering Department, Northeastern University, Boston, MA, USA0000-0003-3231-6715RobS.MacLeodScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Biomedical Engineering Department , University of Utah, Salt Lake City, UT, USA, Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA0000-0002-0000-0356AkilNarayanScientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA, Mathematics Department, University of Utah, Salt Lake City, UT, USA0000-0002-5914-420710.21105/joss.04249https://doi.org/10.5281/zenodo.8226383Pythonhttps://joss.theoj.org/papers/10.21105/joss.04249.pdfuncertainty quantification, computer modeling, polynomial chaos, bioelectricitytag:joss.theoj.org,2005:Paper/44782023-09-15T12:06:34Z2023-09-18T07:49:02ZPyThia: A Python package for Uncertainty Quantification based on non-intrusive polynomial chaos expansionsacceptedv3.1.02023-05-10 13:36:21 UTC892023-09-15 12:06:34 UTC820235489NandoHegemannPhysikalisch-Technische Bundesanstalt, Germany0000-0003-3953-9006SebastianHeidenreichPhysikalisch-Technische Bundesanstalt, Germany0000-0002-1909-577010.21105/joss.05489https://doi.org/10.5281/zenodo.8329459Pythonhttps://joss.theoj.org/papers/10.21105/joss.05489.pdfpolynomial chaos expansion, sensitivity analysis, regression, function approximationtag:joss.theoj.org,2005:Paper/45082023-09-02T03:36:57Z2023-09-03T00:01:27ZGaussianRandomFields.jl: A Julia package to generate and sample from Gaussian random fieldsacceptedv2.2.22023-05-27 17:06:59 UTC892023-09-02 03:36:57 UTC820235595PieterjanRobbeKU Leuven, Belgium0000-0002-6254-824510.21105/joss.05595https://doi.org/10.5281/zenodo.8306255Juliahttps://joss.theoj.org/papers/10.21105/joss.05595.pdfrandom fields, uncertainty quantification, statisticstag:joss.theoj.org,2005:Paper/38282023-07-26T13:30:49Z2023-07-27T00:01:46ZAmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantificationacceptedv0.12022-08-22 16:32:43 UTC872023-07-26 13:30:49 UTC820235035MuhammedShuaibiDepartment of Chemical Engineering, Carnegie Mellon University, United StatesYugeHuDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0003-3648-7749XiangyunLeiDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesBenjaminM.ComerDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesMattAdamsDepartment of Chemical Engineering, Carnegie Mellon University, United StatesJacobParasSchool of Physics and School of Computer Science, Georgia Institute of Technology, United StatesRuiQiChenDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesEricMusaDepartment of Chemical Engineering, University of Michigan, United StatesJosephMusielewiczDepartment of Chemical Engineering, Carnegie Mellon University, United StatesAndrewA.PetersonSchool of Engineering, Brown University, United States0000-0003-2855-9482AndrewJ.MedfordDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0001-8311-9581ZacharyUlissiDepartment of Chemical Engineering, Carnegie Mellon University, United States0000-0002-9401-491810.21105/joss.05035https://doi.org/10.5281/zenodo.8151492Python, C++, C, G-code, GAPhttps://joss.theoj.org/papers/10.21105/joss.05035.pdfmachine learning interatomic potentials, neural networks, molecular dynamicstag:joss.theoj.org,2005:Paper/37402023-03-18T11:05:04Z2023-03-19T00:03:19ZUM-Bridge: Uncertainty quantification and modeling bridgeaccepted1.1.12022-07-22 20:31:01 UTC832023-03-18 11:05:04 UTC820234748LinusSeelingerInstitute for Applied Mathematics, Heidelberg University, Heidelberg, Germany0000-0001-8632-8493VivianCheng-SeelingerIndependent researcherAndrewDavisCourant Institute of Mathematical Sciences, New York Univeristy, New York, NY, USA0000-0002-6023-0989MatthewParnoDepartment of Mathematics, Dartmouth College, Hannover, NH, USA0000-0002-9419-2693AnneReinarzDepartment of Computer Science, Durham University, Durham, United Kingdom0000-0003-1787-763710.21105/joss.04748https://doi.org/10.5281/zenodo.7743819R, Python, PowerShellhttps://joss.theoj.org/papers/10.21105/joss.04748.pdfC++tag:joss.theoj.org,2005:Paper/37592023-02-15T13:28:09Z2023-02-16T00:00:48ZPoUnce: A framework for automatized uncertainty quantification simulations on high-performance clustersacceptedv1.0.02022-08-02 14:41:27 UTC822023-02-15 13:28:09 UTC820234683JakobDuerrwaechterInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany0000-0001-8961-5340ThomasKuhnInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, GermanyFabianMeyerInstitute of Applied Analysis and Numerical Simulation, University of Stuttgart, GermanyAndreaBeckInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany, The Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, GermanyClaus-DieterMunzInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany10.21105/joss.04683https://doi.org/10.5281/zenodo.7600634Pythonhttps://joss.theoj.org/papers/10.21105/joss.04683.pdfUncertainty quantification, High performance computing, Mulitlevel Monte Carlo, Multifidelity Monte Carlotag:joss.theoj.org,2005:Paper/30142022-07-05T20:37:08Z2022-07-06T00:01:04ZUnlockNN: Uncertainty quantification for neural network models of chemical systemsacceptedv2.0.22021-09-03 16:12:59 UTC752022-07-05 20:37:08 UTC720223700AlexanderMoriartyDepartment of Materials, Imperial College London, London, UK0000-0001-7525-1419KazukiMoritaDepartment of Materials, Imperial College London, London, UK0000-0002-2558-6963KeithT.ButlerSciML, STFC Scientific Computing Division, Rutherford Appleton Laboratories, UK0000-0001-5432-5597AronWalshDepartment of Materials, Imperial College London, London, UK, Department of Materials Science and Engineering, Yonsei University, Seoul, Korea0000-0001-5460-703310.21105/joss.03700https://doi.org/10.5281/zenodo.6799685Python, PureBasichttps://joss.theoj.org/papers/10.21105/joss.03700.pdfgraph neural networks, uncertainty quantification, machine learning, material science, chemistry