{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:49:48Z","timestamp":1774068588827,"version":"3.50.1"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T00:00:00Z","timestamp":1583452800000},"content-version":"vor","delay-in-days":65,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007065","name":"Nvidia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["SoftwareX"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1016\/j.softx.2020.100450","type":"journal-article","created":{"date-parts":[[2020,3,12]],"date-time":"2020-03-12T20:53:14Z","timestamp":1584046394000},"page":"100450","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"special_numbering":"C","title":["Pygpc: A sensitivity and uncertainty analysis toolbox for Python"],"prefix":"10.1016","volume":"11","author":[{"given":"Konstantin","family":"Weise","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucas","family":"Po\u00dfner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Gast","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas R.","family":"Kn\u00f6sche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.softx.2020.100450_b1","doi-asserted-by":"crossref","unstructured":"Salvador R, Ramirez F, V\u2019yacheslavovna M, Miranda PC. Effects of tissue dielectric properties on the electric field induced in tDCS: A sensitivity analysis. In: 2012 annual international conference of the IEEE engineering in medicine and biology society. p. 787\u201390. http:\/\/dx.doi.org\/10.1109\/EMBC.2012.6346049.","DOI":"10.1109\/EMBC.2012.6346049"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.neuroimage.2010.07.061","article-title":"Impact of the gyral geometry on the electric field induced by transcranial magnetic stimulation","volume":"54","author":"Thielscher","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.softx.2020.100450_b3","doi-asserted-by":"crossref","unstructured":"Santos L, Martinho M, Salvador R, Wenger C, Fernandes SR, Ripolles O et al. Evaluation of the electric field in the brain during transcranial direct current stimulation: A sensitivity analysis. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). Orlando, FL, 2016. p. 1778\u201381. http:\/\/dx.doi.org\/10.1109\/EMBC.2016.7591062.","DOI":"10.1109\/EMBC.2016.7591062"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b4","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3390\/electronics7030030","article-title":"Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits","volume":"7","author":"Kaintura","year":"2018","journal-title":"Electronics"},{"issue":"9","key":"10.1016\/j.softx.2020.100450_b5","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.fluiddyn.2005.12.003","article-title":"Uncertainty propagation in CFD using polynomial chaos decomposition","volume":"38","author":"Knio","year":"2006","journal-title":"Fluid Dyn Res"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b6","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0021-9991(03)00092-5","article-title":"Modeling uncertainty in flow simulations via generalized polynomial chaos","volume":"187","author":"Xiu","year":"2003","journal-title":"J Comput Phys"},{"key":"10.1016\/j.softx.2020.100450_b7","series-title":"Proceedings of the 44th AiAA aerospace sciences meeting and exhibit, Vol. 891","first-page":"1","article-title":"A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations","author":"Hosder","year":"2006"},{"key":"10.1016\/j.softx.2020.100450_b8","series-title":"Advanced computational methods in heat transfer VIII","first-page":"1","article-title":"Modeling uncertainty in three-dimensional heat transfer problems","volume":"vol. 46","author":"Wan","year":"2004"},{"issue":"24","key":"10.1016\/j.softx.2020.100450_b9","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1016\/S0017-9310(03)00299-0","article-title":"A new stochastic approach to transient heat conduction modeling with uncertainty","volume":"46","author":"Xiu","year":"2003","journal-title":"Int J Heat Mass Transfer"},{"issue":"4","key":"10.1016\/j.softx.2020.100450_b10","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11044-006-9007-5","article-title":"Modeling multibody systems with uncertainties. Part I: Theoretical and computational aspects","volume":"15","author":"Sandu","year":"2006","journal-title":"Multibody Syst Dyn"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b11","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11044-006-9008-4","article-title":"Modeling multibody systems with uncertainties. Part II: Numerical applications","volume":"15","author":"Sandu","year":"2006","journal-title":"Multibody Syst Dyn"},{"issue":"2","key":"10.1016\/j.softx.2020.100450_b12","doi-asserted-by":"crossref","first-page":"412","DOI":"10.4208\/cicp.260512.260912a","article-title":"A polynomial chaos expansion trust region method for robust optimization","volume":"14","author":"Zein","year":"2013","journal-title":"Commun Comput Phys"},{"issue":"2","key":"10.1016\/j.softx.2020.100450_b13","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1137\/S1064827503427741","article-title":"Numerical challenges in the use of polynomial chaos representations for stochastic processes","volume":"26","author":"Debusschere","year":"2004","journal-title":"SIAM J Sci Comput"},{"key":"10.1016\/j.softx.2020.100450_b14","series-title":"Handbook of uncertainty quantification","first-page":"1807","article-title":"The uncertainty quantification toolkit (UQTk)","author":"Debusschere","year":"2016"},{"key":"10.1016\/j.softx.2020.100450_b15","doi-asserted-by":"crossref","unstructured":"Marelli S, Sudret B. UQLab: A framework for uncertainty quantification in matlab. In: Beer Michael, Au Siu-Kui, Hall Jim W. editors. Second international conference on vulnerability and risk analysis and management (ICVRAM) and the sixth international symposium on uncertainty, modeling, and analysis (ISUMA). Liverpool, UK, 2014. p. 2554-63. http:\/\/dx.doi.org\/10.1061\/9780784413609.257.","DOI":"10.1061\/9780784413609.257"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b16","doi-asserted-by":"crossref","DOI":"10.1109\/TMAG.2015.2480046","article-title":"Uncertainty analysis in lorentz force eddy current testing","volume":"52","author":"Weise","year":"2016","journal-title":"IEEE Trans Magn"},{"issue":"7","key":"10.1016\/j.softx.2020.100450_b17","doi-asserted-by":"crossref","DOI":"10.1109\/TMAG.2015.2390593","article-title":"Uncertainty analysis in transcranial magnetic stimulation using non-intrusive polynomial chaos expansion","volume":"51","author":"Weise","year":"2015","journal-title":"IEEE Trans Magn"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b18","doi-asserted-by":"crossref","DOI":"10.1109\/TMAG.2015.2475120","article-title":"Fast MOR-based approach to uncertainty quantification in transcranial magnetic stimulation","volume":"52","author":"Codecasa","year":"2016","journal-title":"IEEE Trans Magn"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b19","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1016\/j.neuroimage.2018.12.053","article-title":"A principled approach to conductivity uncertainty analysis in electric field calculations","volume":"188","author":"Saturnino","year":"2019","journal-title":"NeuroImage"},{"key":"10.1016\/j.softx.2020.100450_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116486","article-title":"A novel approach to localize cortical TMS effects","volume":"209","author":"Weise","year":"2020","journal-title":"Neuroimage"},{"issue":"319","key":"10.1016\/j.softx.2020.100450_b21","first-page":"1","article-title":"Some basic hypergeometric orthogonal polynomials that generalize Jacobi-polynomials","volume":"54","author":"Askey","year":"1985","journal-title":"Mem Amer Math Soc"},{"issue":"1\u20133","key":"10.1016\/j.softx.2020.100450_b22","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0378-4754(00)00270-6","article-title":"Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates","volume":"55","author":"Sobol","year":"2001","journal-title":"Math Comput Simulation"},{"issue":"7","key":"10.1016\/j.softx.2020.100450_b23","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1016\/j.ress.2007.04.002","article-title":"Global sensitivity analysis using polynomial chaos expansions","volume":"93","author":"Sudret","year":"2008","journal-title":"Reliab Eng Syst Saf"},{"issue":"2\u20134","key":"10.1016\/j.softx.2020.100450_b24","first-page":"242","article-title":"Fast numerical methods for stochastic computations: A review","volume":"5","author":"Xiu","year":"2009","journal-title":"Commun Comput Phys"},{"key":"10.1016\/j.softx.2020.100450_b25","series-title":"Version 9.7 (R2019b)","year":"2019"},{"issue":"2","key":"10.1016\/j.softx.2020.100450_b26","first-page":"239","article-title":"A comparison of three methods for selecting values of input variables in the analysis of output from a computer code","volume":"21","author":"Mckay","year":"1979","journal-title":"Technometrics"},{"issue":"5","key":"10.1016\/j.softx.2020.100450_b27","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1214\/aos\/1069362310","article-title":"On Latin hypercube sampling","volume":"24","author":"Loh","year":"1996","journal-title":"Ann Statist"},{"key":"10.1016\/j.softx.2020.100450_b28","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jcp.2015.02.025","article-title":"Enhancing \u21131 -minimization estimates of polynomial chaos expansions using basis selection","volume":"289","author":"Jakeman","year":"2015","journal-title":"J Comput Phys"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b29","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.jcp.2015.12.049","article-title":"On polynomial chaos expansion via gradient-enhanced \u21131-minimization","volume":"310","author":"Peng","year":"2016","journal-title":"J Comput Phys"},{"key":"10.1016\/j.softx.2020.100450_b30","series-title":"Handbook of uncertainty quantification","first-page":"827","article-title":"Compressive sampling methods for sparse polynomial chaos expansions","author":"Hampton","year":"2016"},{"key":"10.1016\/j.softx.2020.100450_b31","series-title":"Theoretical foundations and numerical methods for sparse recovery","isbn-type":"print","first-page":"1","article-title":"Compressive sensing and structured random matrices","volume":"vol. 9","author":"Rauhut","year":"2010","ISBN":"https:\/\/id.crossref.org\/isbn\/9783110226157"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b32","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.jcp.2013.12.009","article-title":"Basis adaptation in homogeneous chaos spaces","volume":"259","author":"Tipireddy","year":"2014","journal-title":"J Comput Phys"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jcp.2018.12.010","article-title":"Compressive sensing adaptation for polynomial chaos expansions","volume":"380","author":"Tsilifis","year":"2019","journal-title":"J Comput Phys"},{"issue":"2","key":"10.1016\/j.softx.2020.100450_b34","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans Inform Theory"},{"issue":"1\u20133","key":"10.1016\/j.softx.2020.100450_b35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(89)90049-0","article-title":"Connectionist learning procedures","volume":"40","author":"Hinton","year":"1989","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.softx.2020.100450_b36","unstructured":"Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics. 2010. p. 249\u201356."},{"key":"10.1016\/j.softx.2020.100450_b37","series-title":"Proceedings of the 2015 ieee international conference on computer vision (ICCV)","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b38","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"issue":"6","key":"10.1016\/j.softx.2020.100450_b39","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1073\/pnas.0811168106","article-title":"Predicting human resting-state functional connectivity from structural connectivity","volume":"106","author":"Honey","year":"2009","journal-title":"Proc Natl Acad Sci USA"},{"issue":"2","key":"10.1016\/j.softx.2020.100450_b40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10548-010-0140-3","article-title":"Connecting mean field models of neural activity to EEG and fMRI data","volume":"23","author":"Bojak","year":"2010","journal-title":"Brain Topogr"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b41","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1016\/j.neuroimage.2009.12.068","article-title":"Computational and dynamic models in neuroimaging","volume":"52","author":"Friston","year":"2010","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.softx.2020.100450_b42","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1038\/nn.4497","article-title":"Dynamic models of large-scale brain activity","volume":"20","author":"Breakspear","year":"2017","journal-title":"Nature Neurosci"},{"issue":"8","key":"10.1016\/j.softx.2020.100450_b43","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1000092","article-title":"The dynamic brain: From spiking neurons to neural masses and cortical fields","volume":"4","author":"Deco","year":"2008","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"10.1016\/j.softx.2020.100450_b44","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/BF00199471","article-title":"Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns","volume":"73","author":"Jansen","year":"1995","journal-title":"Biol Cybernet"},{"issue":"4","key":"10.1016\/j.softx.2020.100450_b45","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1016\/j.neuroimage.2005.10.045","article-title":"Dynamic causal modeling of evoked responses in EEG and MEG","volume":"30","author":"David","year":"2006","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.softx.2020.100450_b46","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.neuroimage.2007.08.001","article-title":"Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism","volume":"39","author":"Sotero","year":"2008","journal-title":"NeuroImage"},{"issue":"12","key":"10.1016\/j.softx.2020.100450_b47","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0225900","article-title":"PyRates\u2014A Python framework for rate-based neural simulations","volume":"14","author":"Gast","year":"2019","journal-title":"PloS one"},{"key":"10.1016\/j.softx.2020.100450_b48","series-title":"Motion-induced eddy current techniques for non-destructive testing and evaluation","isbn-type":"print","doi-asserted-by":"crossref","DOI":"10.1049\/PBCE106E","author":"Brauer","year":"2018","ISBN":"https:\/\/id.crossref.org\/isbn\/9781785612169"}],"container-title":["SoftwareX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711020300078?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352711020300078?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T03:49:08Z","timestamp":1762487348000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352711020300078"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":48,"alternative-id":["S2352711020300078"],"URL":"https:\/\/doi.org\/10.1016\/j.softx.2020.100450","relation":{},"ISSN":["2352-7110"],"issn-type":[{"value":"2352-7110","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Pygpc: A sensitivity and uncertainty analysis toolbox for Python","name":"articletitle","label":"Article Title"},{"value":"SoftwareX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.softx.2020.100450","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"simple-article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100450"}}