{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T19:23:52Z","timestamp":1787340232023,"version":"3.56.0"},"reference-count":46,"publisher":"Society for Industrial & Applied Mathematics (SIAM)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIAM\/ASA J. Uncertainty Quantification"],"published-print":{"date-parts":[[2017,1]]},"abstract":"<jats:p>Experimental design is crucial for inference where limitations in the data collection procedure are present due to cost or other restrictions. Optimal experimental designs determine parameters that in some appropriate sense make the data the most informative possible. In a Bayesian setting this is translated to updating to the best possible posterior. Information theoretic arguments have led to the formation of the expected information gain as a design criterion. This can be evaluated mainly by Monte Carlo sampling and maximized by using stochastic approximation methods, both known for being computationally expensive tasks. We propose a framework where a lower bound of the expected information gain is used as an alternative design criterion. In addition to alleviating the computational burden, this also addresses issues concerning estimation bias. The problem of permeability inference in a large contaminated area is used to demonstrate the validity of our approach where we employ the massively parallel version of the multiphase multicomponent simulator TOUGH2 to simulate contaminant transport and a polynomial chaos approximation of the forward model that further accelerates the objective function evaluations. The proposed methodology is demonstrated in a setting where field measurements are available.<\/jats:p>","DOI":"10.1137\/15m1043303","type":"journal-article","created":{"date-parts":[[2017,1,5]],"date-time":"2017-01-05T12:24:30Z","timestamp":1483619070000},"page":"30-62","source":"Crossref","is-referenced-by-count":35,"title":["Efficient Bayesian Experimentation Using an Expected Information Gain Lower Bound"],"prefix":"10.1137","volume":"5","author":[{"given":"Panagiotis","family":"Tsilifis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roger G.","family":"Ghanem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paris","family":"Hajali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"351","published-online":{"date-parts":[[2017,1,5]]},"reference":[{"key":"atypb1","first-page":"27","volume":"6","year":"2009","journal-title":"GMS Groundwater Modeling System Version 6.5"},{"key":"atypb2","doi-asserted-by":"crossref","unstructured":"A.C. 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Spanos,\n                      Stochastic Finite Elements: A Spectral Approach\n                      , rev. ed., Dover, Mineola, NY, 2003."},{"key":"atypb9","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-9399(2002)128:1(66)"},{"key":"atypb10","unstructured":"P. Glasserman,\n                      Gradient Estimation via Perturbation Analysis\n                      , Springer, Berlin, 1990."},{"key":"atypb11","doi-asserted-by":"publisher","DOI":"10.2307\/3318737"},{"key":"atypb12","first-page":"871","author":"Haddad-Zadegan H.","year":"2013","journal-title":"Netherlands"},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2015.02.006"},{"key":"atypb14","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/57.1.97"},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1007\/BF00933971"},{"key":"atypb16","doi-asserted-by":"crossref","unstructured":"S. Hosder, R.W. Walters, and M. Balch,\n                      Efficient sampling for non-intrusive polynomial chaos applications with multiple uncertain input variables\n                      , in 9th AIAA Non-Deterministic Approaches Conference, AIAA, Reston, VA, 2007, AIAA 2007-1939.","DOI":"10.2514\/6.2007-1939"},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2012.08.013"},{"key":"atypb18","doi-asserted-by":"publisher","DOI":"10.1615\/Int.J.UncertaintyQuantification.2014006730"},{"key":"atypb19","doi-asserted-by":"publisher","DOI":"10.1029\/93WR00547"},{"key":"atypb20","doi-asserted-by":"publisher","DOI":"10.1029\/94WR01972"},{"key":"atypb21","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729392"},{"key":"atypb22","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"atypb23","doi-asserted-by":"publisher","DOI":"10.1029\/2006WR005673"},{"key":"atypb24","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177728069"},{"key":"atypb25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2013.02.017"},{"key":"atypb26","doi-asserted-by":"publisher","DOI":"10.1029\/WR023i002p00351"},{"key":"atypb27","doi-asserted-by":"publisher","DOI":"10.1063\/1.1699114"},{"key":"atypb28","doi-asserted-by":"publisher","DOI":"10.1002\/env.787"},{"key":"atypb29","unstructured":"C. Oldenburg and K. Pruess,\n                      EOS$7$R: Radionuclide Transport for TOUGH\n                      2, Report LBL-34868, Lawrence Berkeley Laboratory, Berkeley, CA, 1995."},{"key":"atypb30","unstructured":"K. Pruess, C. Oldenburg, and G. Moridis,\n                      TOUGH$2$ User's Guide, Version\n                      2, Report LBNL-43134, Lawrence Berkeley Laboratory, Berkeley, CA, 1999."},{"key":"atypb31","doi-asserted-by":"crossref","unstructured":"C.E. Rasmussen and C.K.I. Williams,\n                      Gaussian Processes for Machine Learning\n                      , MIT Press, Cambridge, MA, 2006.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"atypb32","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"atypb33","doi-asserted-by":"publisher","DOI":"10.1198\/1061860032012"},{"key":"atypb34","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00225"},{"key":"atypb35","doi-asserted-by":"crossref","unstructured":"G.A.F. Seber and A.J. 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Sullivan,\n                      Introduction to Uncertainty Quantification\n                      , Springer, Cham, Switzerland, 2015.","DOI":"10.1007\/978-3-319-23395-6"},{"key":"atypb41","doi-asserted-by":"publisher","DOI":"10.1115\/1.4034102"},{"key":"atypb42","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-246X.2003.02048.x"},{"key":"atypb43","doi-asserted-by":"publisher","DOI":"10.1137\/S1064827501387826"},{"key":"atypb44","doi-asserted-by":"publisher","DOI":"10.1016\/S0021-9991(03)00092-5"},{"key":"atypb45","unstructured":"K. Zhang, Y.S. Wu, and K. Pruess,\n                      User's Guide for TOUGH$2$-MP - A Massively Parallel Version of the TOUGH2 Code\n                      , Report LBNL-315E, Lawrence Berkeley Laboratory, Berkeley, CA, 2008."},{"key":"atypb46","doi-asserted-by":"publisher","DOI":"10.1021\/ie300856p"}],"container-title":["SIAM\/ASA Journal on Uncertainty Quantification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/15M1043303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T18:26:11Z","timestamp":1787336771000},"score":1,"resource":{"primary":{"URL":"https:\/\/epubs.siam.org\/doi\/10.1137\/15M1043303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["10.1137\/15M1043303"],"URL":"https:\/\/doi.org\/10.1137\/15m1043303","relation":{},"ISSN":["2166-2525"],"issn-type":[{"value":"2166-2525","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1]]}}}