{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T12:07:29Z","timestamp":1779278849815,"version":"3.51.4"},"reference-count":88,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Science"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.jocs.2026.102857","type":"journal-article","created":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:20:41Z","timestamp":1775316041000},"page":"102857","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Statistically-informed surrogate models combining linear dimension reduction and neural networks for blast wave propagation"],"prefix":"10.1016","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8223-9335","authenticated-orcid":false,"given":"Pierre","family":"Sochala","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Souveton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Terrana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.jocs.2026.102857_b1","series-title":"Adaptive Control Processes: A Guided Tour","author":"Bellman","year":"1961"},{"key":"10.1016\/j.jocs.2026.102857_b2","series-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"Hastie","year":"2009"},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b3","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Ser. B"},{"key":"10.1016\/j.jocs.2026.102857_b4","first-page":"33","article-title":"Atomic decomposition by basis pursuit","volume":"20","author":"Chen","year":"1998","journal-title":"JSISC"},{"issue":"2","key":"10.1016\/j.jocs.2026.102857_b5","first-page":"407","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"AnnStat"},{"issue":"12","key":"10.1016\/j.jocs.2026.102857_b6","first-page":"4655","article-title":"Signal recovery from random measurements via orthogonal matching pursuit","volume":"53","author":"Tropp","year":"2007","journal-title":"IEEE"},{"issue":"5","key":"10.1016\/j.jocs.2026.102857_b7","first-page":"2230","article-title":"Subspace pursuit for compressive sensing signal reconstruction","volume":"55","author":"Dai","year":"2009","journal-title":"IEEE"},{"key":"10.1016\/j.jocs.2026.102857_b8","series-title":"Gaussian Processes for Machine Learning","author":"Rasmussen","year":"2006"},{"key":"10.1016\/j.jocs.2026.102857_b9","series-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.jocs.2026.102857_b10","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.jocs.2026.102857_b11","series-title":"Proceedings of the 32nd International Conference on Machine Learning","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume":"37","author":"Sohl-Dickstein","year":"2015"},{"key":"10.1016\/j.jocs.2026.102857_b12","series-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","article-title":"Denoising diffusion probabilistic models","author":"Ho","year":"2020"},{"issue":"57","key":"10.1016\/j.jocs.2026.102857_b13","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jocs.2026.102857_b14","series-title":"International Conference on Learning Representations","article-title":"Density estimation using real NVP","author":"Dinh","year":"2017"},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b15","first-page":"2859","article-title":"Linear dimensionality reduction: survey, insights, and generalizations","volume":"16","author":"Cunningham","year":"2015","journal-title":"JMLR"},{"key":"10.1016\/j.jocs.2026.102857_b16","series-title":"Nonlinear Dimensionality Reduction","author":"Lee","year":"2007"},{"key":"10.1016\/j.jocs.2026.102857_b17","first-page":"66","article-title":"Dimensionality reduction: a comparative review","volume":"10","author":"Van Der Maaten","year":"2009","journal-title":"JMLR"},{"issue":"11","key":"10.1016\/j.jocs.2026.102857_b18","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Phil. Mag."},{"key":"10.1016\/j.jocs.2026.102857_b19","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1037\/h0070888","article-title":"Analysis of a complex of statistical variables into principal components.","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"issue":"2065","key":"10.1016\/j.jocs.2026.102857_b20","doi-asserted-by":"crossref","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Phil. Trans. R. Soc. A"},{"key":"10.1016\/j.jocs.2026.102857_b21","series-title":"Encyclopedia of Statistical Sciences","first-page":"581","article-title":"Partial Least Squares","volume":"vol. 6","author":"Wold","year":"1985"},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b22","first-page":"187","article-title":"Analysis of two partial-least-squares algorithms for multivariate calibration","volume":"2","author":"Manne","year":"1987","journal-title":"ChemonILS"},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b23","first-page":"211","article-title":"PLS regression methods","volume":"2","author":"H\u00f6skuldsson","year":"1988","journal-title":"JChemom"},{"key":"10.1016\/j.jocs.2026.102857_b24","series-title":"Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies","author":"Constantine","year":"2015"},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b25","first-page":"A534","article-title":"Gradient-based dimension reduction of multivariate vector-valued functions","volume":"42","author":"Zahm","year":"2020","journal-title":"JSISC"},{"issue":"4","key":"10.1016\/j.jocs.2026.102857_b26","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/s41019-022-00193-5","article-title":"Dimensionality reduction in surrogate modeling: A review of combined methods","volume":"7","author":"Hou","year":"2022","journal-title":"Data Sci. Eng."},{"issue":"2","key":"10.1016\/j.jocs.2026.102857_b27","first-page":"101","article-title":"Model reduction for large-scale earthquake simulation in an uncertain 3D medium","volume":"10","author":"Sochala","year":"2020","journal-title":"IJUQ"},{"key":"10.1016\/j.jocs.2026.102857_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2021.108246","article-title":"A global surrogate model for high-dimensional structural systems based on partial least squares and kriging","volume":"164","author":"Liu","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.jocs.2026.102857_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.strusafe.2023.102404","article-title":"Adaptive active subspace-based metamodeling for high-dimensional reliability analysis","volume":"106","author":"Kim","year":"2024","journal-title":"Struct. Saf."},{"key":"10.1016\/j.jocs.2026.102857_b30","series-title":"ICLR","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2014"},{"key":"10.1016\/j.jocs.2026.102857_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2021.107390","article-title":"Fusing sufficient dimension reduction with neural networks","volume":"168","author":"Kapla","year":"2022","journal-title":"Comput. Statist. Data Anal."},{"key":"10.1016\/j.jocs.2026.102857_b32","series-title":"Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid flow","author":"Gonzalez","year":"2018"},{"key":"10.1016\/j.jocs.2026.102857_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2021.114181","article-title":"POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition","volume":"388","author":"Fresca","year":"2022","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.jocs.2026.102857_b34","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s42256-021-00302-5","article-title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators","volume":"3","author":"Lu","year":"2021","journal-title":"Nat. Mach. Intell"},{"key":"10.1016\/j.jocs.2026.102857_b35","series-title":"Fourier neural operator for parametric partial differential equations","author":"Li","year":"2021"},{"key":"10.1016\/j.jocs.2026.102857_b36","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5802\/smai-jcm.74","article-title":"Model reduction and neural networks for parametric PDEs","volume":"7","author":"Bhattacharya","year":"2021","journal-title":"SMAI J. Comput. Math."},{"issue":"1006","key":"10.1016\/j.jocs.2026.102857_b37","first-page":"322","article-title":"The diffraction of sound pulses I: Diffraction by a semi-infinite plane","volume":"186","author":"Friedlander","year":"1946","journal-title":"Proc. R. Soc. Lond. A Math. Phys. Sci."},{"key":"10.1016\/j.jocs.2026.102857_b38","series-title":"Explosive Shocks in Air","author":"Kinney","year":"2013"},{"issue":"12","key":"10.1016\/j.jocs.2026.102857_b39","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/j.ijimpeng.2006.11.003","article-title":"Predicting the effectiveness of blast wall barriers using neural networks","volume":"34","author":"Remennikov","year":"2007","journal-title":"Int. J. Impact Eng."},{"issue":"2","key":"10.1016\/j.jocs.2026.102857_b40","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1260\/2041-4196.2.2.159","article-title":"A neural-network model-based engineering tool for blast wall protection of structures","volume":"2","author":"Bewick","year":"2011","journal-title":"Int. J. Prot. Struct."},{"key":"10.1016\/j.jocs.2026.102857_b41","doi-asserted-by":"crossref","first-page":"26114","DOI":"10.1109\/ACCESS.2023.3257345","article-title":"Prediction of peak pressure by blast wave propagation between buildings using a conditional 3D convolutional neural network","volume":"11","author":"Kang","year":"2023","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b42","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1177\/20414196221144067","article-title":"Prediction of blast loading on protruded structures using machine learning methods","volume":"15","author":"Zahedi","year":"2024","journal-title":"Int. J. Prot. Struct."},{"key":"10.1016\/j.jocs.2026.102857_b43","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.psep.2023.02.008","article-title":"Prediction of BLEVE loads on structures using machine learning and CFD","volume":"171","author":"Li","year":"2023","journal-title":"Process. Saf. Env. Prot."},{"key":"10.1016\/j.jocs.2026.102857_b44","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.eng.2025.03.007","article-title":"BlastGraphNet: An intelligent computational method for the precise and rapid prediction of blast loads on complex 3D buildings using graph neural networks","volume":"49","author":"Wang","year":"2025","journal-title":"Engineering"},{"key":"10.1016\/j.jocs.2026.102857_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124614","article-title":"Real-time gas explosion prediction at urban scale by GIS and graph neural network","volume":"377","author":"Shi","year":"2025","journal-title":"Appl. Energy"},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b46","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1177\/20414196231177364","article-title":"The direction-encoded neural network: a machine learning approach to rapidly predict blast loading in obstructed environments","volume":"15","author":"Dennis","year":"2024","journal-title":"Int. J. Prot. Struct."},{"key":"10.1016\/j.jocs.2026.102857_b47","series-title":"Blast Waves","author":"Needham","year":"2010"},{"key":"10.1016\/j.jocs.2026.102857_b48","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s00193-017-0711-2","article-title":"Direct simulations of outdoor blast wave propagation from source to receiver","volume":"27","author":"Nguyen-Dinh","year":"2017","journal-title":"Shock Waves"},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b49","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3397\/1.2839248","article-title":"Propagation of shock waves from source to receiver","volume":"53","author":"van der Eerden","year":"2005","journal-title":"Noise Control Eng. J."},{"issue":"04","key":"10.1016\/j.jocs.2026.102857_b50","doi-asserted-by":"crossref","DOI":"10.1142\/S2591728518500196","article-title":"A one-way coupled Euler and parabolic model for outdoor blast wave simulation in real environment","volume":"26","author":"Nguyen-Dinh","year":"2018","journal-title":"J. Theor. Comput. Acoust."},{"key":"10.1016\/j.jocs.2026.102857_b51","article-title":"Predicting terrain effects on blast waves: an artificial neural network approach","author":"Leconte","year":"2024","journal-title":"Shock Waves"},{"key":"10.1016\/j.jocs.2026.102857_b52","article-title":"Machine learning prediction of BLEVE loading with graph neural networks","volume":"241","author":"Li","year":"2024","journal-title":"RESS"},{"key":"10.1016\/j.jocs.2026.102857_b53","series-title":"Proceedings of the 19th International Symposium on Interaction of the Effects of Munitions with Structures","article-title":"A direction-encoded machine learning approach for peak overpressure prediction in urban environments","author":"Dennis","year":"2024"},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b54","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/LSP.2008.2010819","article-title":"Weighted subspace distance and its applications to object recognition and retrieval with image sets","volume":"16","author":"Li","year":"2009","journal-title":"IEEE Signal Process. Lett."},{"key":"10.1016\/j.jocs.2026.102857_b55","series-title":"In Multivariate Analysis","first-page":"391","article-title":"Estimation of principal components and related models by iterative least squares","volume":"vol. 59","author":"Wold","year":"1966"},{"key":"10.1016\/j.jocs.2026.102857_b56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1214\/19-SS125","article-title":"PLS for Big Data: A unified parallel algorithm for regularised group PLS","volume":"13","author":"Lafaye de Micheaux","year":"2019","journal-title":"Stat. Surv."},{"issue":"2","key":"10.1016\/j.jocs.2026.102857_b57","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/03610918808812681","article-title":"On the structure of partial least squares regression","volume":"17","author":"Helland","year":"1988","journal-title":"Commun. Stat. Simul. Comput."},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b58","first-page":"79","article-title":"Model selection for partial least squares regression","volume":"64","author":"Li","year":"2002","journal-title":"ChemonILS"},{"key":"10.1016\/j.jocs.2026.102857_b59","first-page":"64","article-title":"A new model selection criterion for partial least squares regression","volume":"169","author":"Mart\u00ednez","year":"2017","journal-title":"ChemonILS"},{"issue":"4","key":"10.1016\/j.jocs.2026.102857_b60","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/00401706.1978.10489693","article-title":"Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models","volume":"20","author":"Wold","year":"1978","journal-title":"Technometrics"},{"key":"10.1016\/j.jocs.2026.102857_b61","series-title":"Multivariate Analysis of Quality. An Introduction","author":"Martens","year":"2001"},{"issue":"494","key":"10.1016\/j.jocs.2026.102857_b62","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1198\/jasa.2011.tm10107","article-title":"The degrees of freedom of partial least squares regression","volume":"106","author":"Kr\u00e4mer","year":"2011","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.jocs.2026.102857_b63","article-title":"Extension and significance testing of Variable Importance in Projection (VIP) indices in Partial Least Squares regression and Principal Components Analysis","volume":"242","author":"Mahieu","year":"2023","journal-title":"ChemonILS"},{"issue":"7","key":"10.1016\/j.jocs.2026.102857_b64","first-page":"368","article-title":"PLS score\u2013loading correspondence and a bi-orthogonal factorization","volume":"16","author":"Ergon","year":"2002","journal-title":"JChemom"},{"issue":"5","key":"10.1016\/j.jocs.2026.102857_b65","first-page":"301","article-title":"Analysis of multiblock and hierarchical PCA and PLS models","volume":"12","author":"Westerhuis","year":"1998","journal-title":"JChemom"},{"issue":"6","key":"10.1016\/j.jocs.2026.102857_b66","first-page":"323","article-title":"A framework for sequential multiblock component methods","volume":"17","author":"Smilde","year":"2003","journal-title":"JChemom"},{"key":"10.1016\/j.jocs.2026.102857_b67","series-title":"The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain","author":"Rosenblatt","year":"1958"},{"key":"10.1016\/j.jocs.2026.102857_b68","series-title":"Perceptrons: An Introduction to Computational Geometry","author":"Minsky","year":"1969"},{"issue":"6088","key":"10.1016\/j.jocs.2026.102857_b69","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"10.1016\/j.jocs.2026.102857_b70","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation by superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math. Control. Signals Syst."},{"issue":"5","key":"10.1016\/j.jocs.2026.102857_b71","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"10.1016\/j.jocs.2026.102857_b72","series-title":"Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences","author":"Werbos","year":"1974"},{"issue":"536","key":"10.1016\/j.jocs.2026.102857_b73","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1080\/01621459.2020.1758115","article-title":"A new coefficient of correlation","volume":"116","author":"Chatterjee","year":"2021","journal-title":"J. Amer. Statist. Assoc."},{"key":"10.1016\/j.jocs.2026.102857_b74","series-title":"Adaptive Mesh Refinement-Theory and Applications: Proceedings of the Chicago Workshop on Adaptive Mesh Refinement Methods, Sept. 3\u20135, 2003","first-page":"283","article-title":"HERA: a hydrodynamic AMR platform for multi-physics simulations","author":"Jourdren","year":"2005"},{"key":"10.1016\/j.jocs.2026.102857_b75","doi-asserted-by":"crossref","unstructured":"S. Terrana, S. Jaouen, F. Duboc, Numerical Simulation of Blast Waves Propagation in a Large Urban Environment: the 2020 Beirut Explosion, in: Procedings of the 34th International Symposium on Shock Waves (ISSW34), 2024.","DOI":"10.1007\/978-981-96-4775-0_30"},{"key":"10.1016\/j.jocs.2026.102857_b76","series-title":"British ordnance board minutes 13565","author":"Hopkinson","year":"1915"},{"key":"10.1016\/j.jocs.2026.102857_b77","series-title":"RGE ALTI\u00ae Digital Elevation Model","author":"Institut national de l\u2019information g\u00e9ographique et foresti\u00e8re (IGN)","year":"2021"},{"key":"10.1016\/j.jocs.2026.102857_b78","series-title":"Structures to resist the effects of accidental explosions","author":"Department of Defense","year":"2008"},{"key":"10.1016\/j.jocs.2026.102857_b79","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017"},{"issue":"12","key":"10.1016\/j.jocs.2026.102857_b80","doi-asserted-by":"crossref","first-page":"10558","DOI":"10.1109\/TPAMI.2024.3447085","article-title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","volume":"46","author":"Cheng","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b81","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11634-011-0090-y","article-title":"Correcting Jaccard and other similarity indices for chance agreement in cluster analysis","volume":"5","author":"Albatineh","year":"2011","journal-title":"Adv. Data Anal. Classif."},{"key":"10.1016\/j.jocs.2026.102857_b82","series-title":"Applied Smoothing Techniques for Data Analysis","author":"Bowman","year":"1997"},{"key":"10.1016\/j.jocs.2026.102857_b83","first-page":"407","article-title":"Sensitivity estimates for nonlinear mathematical models","volume":"1","author":"Sobol","year":"1993","journal-title":"Math. Model. Comput. Exp."},{"issue":"1\u20133","key":"10.1016\/j.jocs.2026.102857_b84","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":"2","key":"10.1016\/j.jocs.2026.102857_b85","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1137\/15M1025621","article-title":"Sensitivity Analysis Based on Cram\u00e9r\u2013von Mises Distance","volume":"6","author":"Gamboa","year":"2018","journal-title":"JUQ"},{"key":"10.1016\/j.jocs.2026.102857_b86","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.sigpro.2019.03.002","article-title":"A class of multidimensional NIPALS algorithms for quaternion and tensor partial least squares regression","volume":"160","author":"Stott","year":"2019","journal-title":"Signal Process."},{"issue":"3","key":"10.1016\/j.jocs.2026.102857_b87","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/0169-7439(93)85002-X","article-title":"SIMPLS: an alternative approach to partial least squares regression","volume":"18","author":"De Jong","year":"1993","journal-title":"Chemometr. Intell. Lab. Syst."},{"issue":"1","key":"10.1016\/j.jocs.2026.102857_b88","first-page":"1","article-title":"Bootstrap Methods: Another Look at the Jackknife","volume":"7","author":"Efron","year":"1979","journal-title":"AnnStat"}],"container-title":["Journal of Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S187775032600075X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S187775032600075X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:28:30Z","timestamp":1779276510000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S187775032600075X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":88,"alternative-id":["S187775032600075X"],"URL":"https:\/\/doi.org\/10.1016\/j.jocs.2026.102857","relation":{},"ISSN":["1877-7503"],"issn-type":[{"value":"1877-7503","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Statistically-informed surrogate models combining linear dimension reduction and neural networks for blast wave propagation","name":"articletitle","label":"Article Title"},{"value":"Journal of Computational Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jocs.2026.102857","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102857"}}