{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:06:50Z","timestamp":1788368810828,"version":"build-2803163510"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T00:00:00Z","timestamp":1545091200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T00:00:00Z","timestamp":1545091200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1404823"],"award-info":[{"award-number":["1404823"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sci Comput"],"published-print":{"date-parts":[[2019,5]]},"DOI":"10.1007\/s10915-018-0894-4","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T05:17:25Z","timestamp":1545110245000},"page":"1335-1359","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Sensitivity-Driven Adaptive Construction of Reduced-space Surrogates"],"prefix":"10.1007","volume":"79","author":[{"given":"Manav","family":"Vohra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alen","family":"Alexanderian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cosmin","family":"Safta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1969-2388","authenticated-orcid":false,"given":"Sankaran","family":"Mahadevan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,12,18]]},"reference":[{"issue":"2","key":"894_CR1","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1137\/S1064827501387826","volume":"24","author":"D Xiu","year":"2002","unstructured":"Xiu, D., Karniadakis, G.E.: The Wiener\u2013Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2), 619\u2013644 (2002)","journal-title":"SIAM J. Sci. Comput."},{"key":"894_CR2","volume-title":"Stochastic Finite Elements: A Spectral Approach","author":"RG Ghanem","year":"2003","unstructured":"Ghanem, R.G., Spanos, P.D.: Stochastic Finite Elements: A Spectral Approach. Courier Corporation, North Chelmsford (2003)"},{"key":"894_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-90-481-3520-2","volume-title":"Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics","author":"O Le Ma\u00eetre","year":"2010","unstructured":"Le Ma\u00eetre, O., Knio, O.M.: Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics. Springer, Berlin (2010)"},{"key":"894_CR4","unstructured":"Friedman, J.H.: Fast MARS. Technical Report 110, Laboratory for Computational Statistics, Department of Statistics, Stanford University (1993)"},{"key":"894_CR5","series-title":"Advanced Lectures on Machine Learning","first-page":"63","volume-title":"Gaussian Processes in Machine Learning","author":"CE Rasmussen","year":"2004","unstructured":"Rasmussen, C.E.: Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning, pp. 63\u201371. Springer, Berlin (2004)"},{"key":"894_CR6","volume-title":"Interpolation of Spatial Data: Some Theory for Kriging","author":"ML Stein","year":"2012","unstructured":"Stein, M.L.: Interpolation of Spatial Data: Some Theory for Kriging. Springer, Berlin (2012)"},{"issue":"3","key":"894_CR7","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/0893-6080(89)90003-8","volume":"2","author":"K Funahashi","year":"1989","unstructured":"Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2(3), 183\u2013192 (1989)","journal-title":"Neural Netw."},{"issue":"1","key":"894_CR8","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/0893-6080(90)90049-Q","volume":"3","author":"DF Specht","year":"1990","unstructured":"Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109\u2013118 (1990)","journal-title":"Neural Netw."},{"key":"894_CR9","first-page":"407","volume":"1","author":"IM Sobol\u2019","year":"1993","unstructured":"Sobol\u2019, I.M.: Sensitivity estimates for nonlinear mathematical models. Math. Model. Comput. Exp. 1, 407\u2013414 (1993)","journal-title":"Math. Model. Comput. Exp."},{"issue":"3","key":"894_CR10","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s10596-012-9286-2","volume":"16","author":"A Alexanderian","year":"2012","unstructured":"Alexanderian, A., Winokur, J., Sraj, I., Srinivasan, A., Iskandarani, M., Thacker, W.C., Knio, O.M.: Global sensitivity analysis in an ocean general circulation model: a sparse spectral projection approach. Comput. Geosci. 16(3), 757\u2013778 (2012)","journal-title":"Comput. Geosci."},{"issue":"5","key":"894_CR11","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1007\/s10596-016-9581-4","volume":"20","author":"G Li","year":"2016","unstructured":"Li, G., Iskandarani, M., Le H\u00e9naff, M., Winokur, J., Le Ma\u00eetre, O.P., Knio, O.M.: Quantifying initial and wind forcing uncertainties in the Gulf of Mexico. Comput. Geosci. 20(5), 1133\u20131153 (2016)","journal-title":"Comput. Geosci."},{"key":"894_CR12","doi-asserted-by":"publisher","first-page":"39536","DOI":"10.1038\/srep39536","volume":"6","author":"A Namhata","year":"2016","unstructured":"Namhata, A., Oladyshkin, S., Dilmore, R.M., Zhang, L., Nakles, D.V.: Probabilistic assessment of above zone pressure predictions at a geologic carbon storage site. Sci. Rep. 6, 39536 (2016)","journal-title":"Sci. Rep."},{"key":"894_CR13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ress.2015.11.005","volume":"147","author":"G Deman","year":"2016","unstructured":"Deman, G., Konakli, K., Sudret, B., Kerrou, J., Perrochet, P., Benabderrahmane, H.: Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model. Reliab. Eng. Syst. Saf. 147, 156\u2013169 (2016)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"894_CR14","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.apm.2017.09.016","volume":"53","author":"B Saad","year":"2018","unstructured":"Saad, B., Alexanderian, A., Prudhomme, S., Knio, O.M.: Probabilistic modeling and global sensitivity analysis for $$co\\_2$$ storage in geological formations: a spectral approach. Appl. Math. Model. 53, 584\u2013601 (2018)","journal-title":"Appl. Math. Model."},{"key":"894_CR15","series-title":"Formal Methods for Computational Systems Biology","first-page":"1","volume-title":"Sensitivity Analysis of Stochastic Models of Bistable Biochemical Reactions","author":"A Degasperi","year":"2008","unstructured":"Degasperi, A., Gilmore, S.: Sensitivity Analysis of Stochastic Models of Bistable Biochemical Reactions. Formal Methods for Computational Systems Biology, pp. 1\u201320. Springer, Berlin (2008)"},{"issue":"24","key":"894_CR16","doi-asserted-by":"publisher","first-page":"244106","DOI":"10.1063\/1.4971797","volume":"145","author":"M Navarro Jimenez","year":"2016","unstructured":"Navarro Jimenez, M., Le Ma\u00eetre, O.P., Knio, O.M.: Global sensitivity analysis in stochastic simulators of uncertain reaction networks. J. Chem. Phys. 145(24), 244106 (2016)","journal-title":"J. Chem. Phys."},{"issue":"23","key":"894_CR17","doi-asserted-by":"publisher","first-page":"233501","DOI":"10.1063\/1.4903816","volume":"116","author":"M Vohra","year":"2014","unstructured":"Vohra, M., Winokur, J., Overdeep, K.R., Marcello, P., Weihs, T.P., Knio, O.M.: Development of a reduced model of formation reactions in zr\u2013al nanolaminates. J. Appl. Phys. 116(23), 233501 (2014)","journal-title":"J. Appl. Phys."},{"issue":"10","key":"894_CR18","doi-asserted-by":"publisher","first-page":"3009","DOI":"10.1016\/j.matcom.2009.01.023","volume":"79","author":"IM Sobol\u2019","year":"2009","unstructured":"Sobol\u2019, I.M., Kucherenko, S.: Derivative based global sensitivity measures and their link with global sensitivity indices. Math. Comput. Simul. 79(10), 3009\u20133017 (2009)","journal-title":"Math. Comput. Simul."},{"issue":"6","key":"894_CR19","doi-asserted-by":"publisher","first-page":"7745","DOI":"10.1016\/j.sbspro.2010.05.208","volume":"2","author":"IM Sobol","year":"2010","unstructured":"Sobol, I.M., Kucherenko, S.: Derivative based global sensitivity measures. Procedia Soc. Behav. Sci. 2(6), 7745\u20137746 (2010)","journal-title":"Procedia Soc. Behav. Sci."},{"key":"894_CR20","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.matcom.2013.02.002","volume":"87","author":"M Lamboni","year":"2013","unstructured":"Lamboni, M., Iooss, B., Popelin, A.L., Gamboa, F.: Derivative-based global sensitivity measures: general links with sobol\u2019 indices and numerical tests. Math. Comput. Simul. 87, 45\u201354 (2013)","journal-title":"Math. Comput. Simul."},{"issue":"7","key":"894_CR21","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1016\/j.ress.2008.05.006","volume":"94","author":"S Kucherenko","year":"2009","unstructured":"Kucherenko, S., Rodriguez-Fernandez, M., Pantelides, C., Shah, N.: Monte Carlo evaluation of derivative-based global sensitivity measures. Reliab. Eng. Syst. Saf. 94(7), 1135\u20131148 (2009)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"894_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12385-1_36","volume-title":"Derivative-Based Global Sensitivity Measures","author":"S Kucherenko","year":"2017","unstructured":"Kucherenko, S., Iooss, B.: Derivative-Based Global Sensitivity Measures. Springer, Berlin (2017)"},{"issue":"15","key":"894_CR23","doi-asserted-by":"publisher","first-page":"7168","DOI":"10.1021\/ie900139x","volume":"48","author":"A Kiparissides","year":"2009","unstructured":"Kiparissides, A., Kucherenko, S.S., Mantalaris, A., Pistikopoulos, E.N.: Global sensitivity analysis challenges in biological systems modeling. Ind. Eng. Chem. Res. 48(15), 7168\u20137180 (2009)","journal-title":"Ind. Eng. Chem. Res."},{"issue":"3","key":"894_CR24","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/BF01061285","volume":"3","author":"A Jameson","year":"1988","unstructured":"Jameson, A.: Aerodynamic design via control theory. J. Sci. Comput. 3(3), 233\u2013260 (1988)","journal-title":"J. Sci. Comput."},{"key":"894_CR25","volume-title":"Perspectives in Flow Control and Optimization","author":"MD Gunzburger","year":"2003","unstructured":"Gunzburger, M.D.: Perspectives in Flow Control and Optimization, vol. 5. SIAM, Philadelphia (2003)"},{"key":"894_CR26","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972054","volume-title":"Computational Optimization of Systems Governed by Partial Differential Equations","author":"A Borz\u00ec","year":"2011","unstructured":"Borz\u00ec, A., Schulz, V.: Computational Optimization of Systems Governed by Partial Differential Equations, vol. 8. SIAM, Philadelphia (2011)"},{"key":"894_CR27","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1137\/16M106306X","volume":"5","author":"A Alexanderian","year":"2017","unstructured":"Alexanderian, A., Petra, N., Stadler, G., Ghattas, O.: Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations. SIAM J. Uncertain. Quantif. 5, 1166\u20131192 (2017)","journal-title":"SIAM J. Uncertain. Quantif."},{"issue":"1","key":"894_CR28","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/S0378-4754(00)00270-6","volume":"55","author":"IM Sobol\u2019","year":"2001","unstructured":"Sobol\u2019, I.M.: Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55(1), 271\u2013280 (2001)","journal-title":"Math. Comput. Simul."},{"issue":"2","key":"894_CR29","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","volume":"32","author":"B Efron","year":"2004","unstructured":"Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., et al.: Least angle regression. Ann. Stat. 32(2), 407\u2013499 (2004)","journal-title":"Ann. Stat."},{"key":"894_CR30","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Methodol. 58, 267\u2013288 (1996)","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"issue":"6","key":"894_CR31","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.crme.2008.02.013","volume":"336","author":"G Blatman","year":"2008","unstructured":"Blatman, G., Sudret, B.: Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach. C. R. M\u00e9c. 336(6), 518\u2013523 (2008)","journal-title":"C. R. M\u00e9c."},{"key":"894_CR32","doi-asserted-by":"crossref","unstructured":"Marelli, S., Sudret, B.: UQLab: a framework for uncertainty quantification in MATLAB. In: Proceedings of the 2nd International Conference on Vulnerability, Risk Analysis and Management (ICVRAM 2014), ASCE, Reston, VA (2014)","DOI":"10.1061\/9780784413609.257"},{"key":"894_CR33","unstructured":"Blatman, G.: Adaptive sparse polynomial chaos expansions for uncertainty propagation and sensitivity analysis. Ph.D. thesis, Clermont-Ferrand 2, (2009)"},{"key":"894_CR34","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717761","volume-title":"Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation","author":"A Griewank","year":"2008","unstructured":"Griewank, A., Walther, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, vol. 105. SIAM, Philadelphia (2008)"},{"issue":"3","key":"894_CR35","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1080\/00401706.1993.10485320","volume":"35","author":"MD Morris","year":"1993","unstructured":"Morris, M.D., Mitchell, T.J., Ylvisaker, D.: Bayesian design and analysis of computer experiments: use of derivatives in surface prediction. Technometrics 35(3), 243\u2013255 (1993)","journal-title":"Technometrics"},{"issue":"1\u20133","key":"894_CR36","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1080\/00102209108951759","volume":"79","author":"RA Yetter","year":"1991","unstructured":"Yetter, R.A., Dryer, F.L., Rabitz, H.: A comprehensive reaction mechanism for carbon monoxide\/hydrogen\/oxygen kinetics. Combust. Sci. Technol. 79(1\u20133), 97\u2013128 (1991)","journal-title":"Combust. Sci. Technol."},{"issue":"8","key":"894_CR37","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/0360-3199(95)00138-7","volume":"21","author":"LM Das","year":"1996","unstructured":"Das, L.M.: Hydrogen\u2013oxygen reaction mechanism and its implication to hydrogen engine combustion. Int. J. Hydrog. Energy 21(8), 703\u2013715 (1996)","journal-title":"Int. J. Hydrog. Energy"},{"key":"894_CR38","doi-asserted-by":"publisher","first-page":"3962","DOI":"10.1002\/anie.200705972","volume":"47","author":"B Loges","year":"2008","unstructured":"Loges, B., Boddien, A., Junge, H., Beller, M.: Controlled generation of hydrogen from formic acid amine adducts at room temperature and application in $$\\text{ H }_2\/\\text{ O }_2$$ fuel cells. Angew. Chem. Int. Ed. 47, 3962\u20133965 (2008)","journal-title":"Angew. Chem. Int. Ed."},{"key":"894_CR39","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.jpowsour.2016.05.133","volume":"325","author":"S Cosnier","year":"2016","unstructured":"Cosnier, S., Gross, A.J., Le Goff, A., Holzinger, M.: Recent advances on enzymatic glucose\/oxygen and hydrogen\/oxygen biofuel cells: achievements and limitations. J. Power Sources 325, 252\u2013263 (2016)","journal-title":"J. Power Sources"},{"key":"894_CR40","doi-asserted-by":"crossref","unstructured":"Safta, C., Najm, H.N., Knio, O.M.: Tchem\u2014a software toolkit for the analysis of complex kinetic models. Sandia Report, SAND2011-3282 (2011)","DOI":"10.2172\/1113874"},{"issue":"7","key":"894_CR41","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1016\/j.ress.2008.10.008","volume":"94","author":"T Crestaux","year":"2009","unstructured":"Crestaux, T., Le Maitre, O.P., Martinez, J.-M.: Polynomial chaos expansion for sensitivity analysis. Reliab. Eng. Syst. Saf. 94(7), 1161\u20131172 (2009). Special Issue on Sensitivity Analysis","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"11","key":"894_CR42","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1016\/j.ress.2010.06.015","volume":"95","author":"G Blatman","year":"2010","unstructured":"Blatman, G., Sudret, B.: Efficient computation of global sensitivity indices using sparse polynomial chaos expansions. Reliab. Eng. Syst. Saf. 95(11), 1216\u20131229 (2010)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"7","key":"894_CR43","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.ress.2007.04.002","volume":"93","author":"B Sudret","year":"2008","unstructured":"Sudret, B.: Global sensitivity analysis using polynomial chaos expansions. Reliab. Eng. Syst. Saf. 93(7), 964\u2013979 (2008)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"894_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12385-1_37","volume-title":"Moment-Independent and Reliability-based Importance Measures","author":"E Borgonovo","year":"2017","unstructured":"Borgonovo, E., Iooss, B.: Moment-Independent and Reliability-based Importance Measures. Springer, Berlin (2017)"},{"key":"894_CR45","series-title":"Uncertainty Management in Simulation-Optimization of Complex Systems","first-page":"101","volume-title":"A Review on Global Sensitivity Analysis Methods","author":"B Iooss","year":"2015","unstructured":"Iooss, B., Lema\u00eetre, P.: A Review on Global Sensitivity Analysis Methods. Uncertainty Management in Simulation-Optimization of Complex Systems, pp. 101\u2013122. Springer, Berlin (2015)"}],"container-title":["Journal of Scientific Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-018-0894-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10915-018-0894-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-018-0894-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T08:27:57Z","timestamp":1720859277000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10915-018-0894-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,18]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,5]]}},"alternative-id":["894"],"URL":"https:\/\/doi.org\/10.1007\/s10915-018-0894-4","relation":{},"ISSN":["0885-7474","1573-7691"],"issn-type":[{"value":"0885-7474","type":"print"},{"value":"1573-7691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,18]]},"assertion":[{"value":"31 July 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 November 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}