{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T17:56:53Z","timestamp":1776707813652,"version":"3.51.2"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"U.S. Department of Energy, Office of Science","award":["DE-AC02-06CH11357"],"award-info":[{"award-number":["DE-AC02-06CH11357"]}]},{"name":"Advanced Scientific Computing Research and Office of Nuclear Physics, Scientific Discovery through Advanced Computing (SciDAC) Program through the FASTMath Institute"},{"name":"Argonne National Laboratory during his appointment as a 2021 Wallace Givens Associate"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Math. Softw."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            This article describes PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive\n            <jats:italic>Python toolkit<\/jats:italic>\n            for\n            <jats:italic>model-constrained OED<\/jats:italic>\n            . The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to \u201cenable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.\u201d This article provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate the potential of the package.\n          <\/jats:p>","DOI":"10.1145\/3653071","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T15:19:08Z","timestamp":1710947948000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments"],"prefix":"10.1145","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5092-3503","authenticated-orcid":false,"given":"Abhijit","family":"Chowdhary","sequence":"first","affiliation":[{"name":"Mathematics Department, North Carolina State University, Raleigh, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5548-0265","authenticated-orcid":false,"given":"Shady E.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, Oklahoma, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5940-9247","authenticated-orcid":false,"given":"Ahmed","family":"Attia","sequence":"additional","affiliation":[{"name":"Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/abe10c"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1137\/130933381"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1137\/140992564"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1137\/17M115712X"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974546"},{"key":"e_1_3_1_7_2","volume-title":"Parameter Estimation and Inverse Problems","author":"Aster Richard C.","year":"2018","unstructured":"Richard C. Aster, Brian Borchers, and Clifford H. Thurber. 2018. Parameter Estimation and Inverse Problems. Elsevier."},{"key":"e_1_3_1_8_2","unstructured":"Ahmed Attia. 2023. PyOED Documentation. Retrieved December 6 2023 from https:\/\/web.cels.anl.gov\/~aattia\/pyoed\/index.html"},{"key":"e_1_3_1_9_2","unstructured":"Ahmed Attia. 2023. PyOED GitLab Repository. Retrieved December 6 2023 from https:\/\/gitlab.com\/ahmedattia\/pyoed"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aad210"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1137\/21M1418666"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1137\/21M1404363"},{"key":"e_1_3_1_13_2","unstructured":"Ahmed Attia Sven Leyffer and Todd Munson. 2023. Robust A-optimal experimental design for Bayesian inverse problems. https:\/\/arxiv.org\/abs\/2305.03855."},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25138-7_20"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/fld.4259"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.3934\/geosci.2015.1.41"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-12-629-2019"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1002\/fld.4255"},{"key":"e_1_3_1_19_2","volume-title":"PETSc\/TAO Users Manual","author":"Balay Satish","year":"2022","unstructured":"Satish Balay, Shrirang Abhyankar, Steven Benson, Jed Brown, Peter R. Brune, Kristopher R. Buschelman, Emil Constantinescu, Alp Dener, Jacob Faibussowitsch, William D. Gropp, Mark F. Adams, Lisandro Dalcin, Victor Eijkhout, Dinesh Kaushik, Matthew G. Knepley, Dave A. May, Lois Curfman McInnes, Richard Tran Mills, Todd Munson, Karl Rupp, Patrick Sanan, Barry F. Smith, Stefano Zampini, and Hong Zhang. 2022. PETSc\/TAO Users Manual. Technical Report. Argonne National Laboratory (ANL), Argonne, IL."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.2982"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1137\/19M1245220"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2018.01.053"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2016.12.041"},{"key":"e_1_3_1_24_2","unstructured":"James Bradbury Roy Frostig Peter Hawkins Matthew James Johnson Chris Leary Dougal Maclaurin George Necula Adam Paszke Jake VanderPlas Skye Wanderman-Milne and Qiao Zhang. 2018. JAX: Composable transformations of Python+NumPy programs. Retrieved from http:\/\/github.com\/google\/jax"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1137\/12089586X"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1214\/13-STS421"},{"key":"e_1_3_1_27_2","first-page":"457 pages","volume-title":"Atmospheric Data Analysis","author":"Daley Roger","year":"1991","unstructured":"Roger Daley. 1991. Atmospheric Data Analysis. Cambridge University Press, 457 pages."},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-03711-5"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-4381-7_17"},{"key":"e_1_3_1_30_2","volume-title":"Theory of Optimal Experiments","author":"Fedorov Valerii Vadimovich","year":"2013","unstructured":"Valerii Vadimovich Fedorov. 2013. Theory of Optimal Experiments. Elsevier."},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.11128\/arep.55.a55190"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1137\/090780717"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2607(03)00073-7"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1002\/oca.751"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0065-2687(08)60442-2"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/24\/5\/055012"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/26\/2\/025002"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1214\/13-AAP982"},{"key":"e_1_3_1_39_2","first-page":"908","volume-title":"Proceedings of the 32nd International Conference on Machine Learning","volume":"37","author":"Han Insu","year":"2015","unstructured":"Insu Han, Dmitry Malioutov, and Jinwoo Shin. 2015. Large-scale log-determinant computation through stochastic Chebyshev expansions. In Proceedings of the 32nd International Conference on Machine Learning, Vol. 37. Francis Bach and David Blei (Eds.), PMLR, Lille, France, 908\u2013917."},{"key":"e_1_3_1_40_2","unstructured":"Radoslav Harman and Lenka Filov\u00e1. 2019. A brief introduction to the R library OptimalDesign. https:\/\/cran.r-project.org\/web\/packages\/OptimalDesign\/OptimalDesign.pdf"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1615\/Int.J.UncertaintyQuantification.2014006730"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2012.08.013"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.2172\/1879614"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2022.107680"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011241421041"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1137\/130913110"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1137\/130928315"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2013.02.017"},{"key":"e_1_3_1_49_2","first-page":"40","volume-title":"Proceedings of the Seminar on Predictability","volume":"1","author":"Lorenz Edward N.","year":"1996","unstructured":"Edward N. Lorenz. 1996. Predictability: A problem partly solved. In Proceedings of the Seminar on Predictability, Vol. 1. 40\u201358."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71056-1_2"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2019.03.010"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115730"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114199"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.21236\/ADA555315"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6363-4"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719109"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2008.12.008"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1201\/b10934"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00211-017-0880-z"},{"issue":"2","key":"e_1_3_1_60_2","first-page":"24","article-title":"crossdes: A package for design and randomization in crossover studies","volume":"5","author":"Sailer Oliver","year":"2005","unstructured":"Oliver Sailer. 2005. crossdes: A package for design and randomization in crossover studies. Rnews 5, 2 (2005), 24\u201327.","journal-title":"Rnews"},{"key":"e_1_3_1_61_2","first-page":"7146","article-title":"Understanding controlled trials crossover trials","volume":"316","author":"Sibbald Bonnie","year":"1998","unstructured":"Bonnie Sibbald and Chris Roberts. 1998. Understanding controlled trials crossover trials. Bmj 316, 7146 (1998), 1719\u20131720.","journal-title":"Bmj"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973228"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492910000061"},{"key":"e_1_3_1_64_2","article-title":"AutoOED:","author":"Tian Yunsheng","year":"2021","unstructured":"Yunsheng Tian, Mina Konakovi\u0107 Lukovi\u0107, Timothy Erps, Michael Foshey, and Wojciech Matusik. 2021. AutoOED: Automated optimal experimental design platform with data-and time-efficient multi-objective optimization. (2021). arXiv:2104.05959","journal-title":"Automated optimal experimental design platform with data-and time-efficient multi-objective optimization"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1986.10478240"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1080\/002071700417876"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1080\/16000870.2018.1445364"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00940"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717570"},{"issue":"0","key":"e_1_3_1_70_2","first-page":"1","article-title":"Package \u2018AlgDesign\u2019","volume":"1","author":"Wheeler Bob","year":"2019","unstructured":"Bob Wheeler and Maintainer Jerome Braun. 2019. Package \u2018AlgDesign\u2019. The R Project for Statistical Computing 1, 0 (2019), 1\u201325.","journal-title":"The R Project for Statistical Computing"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-023-02145-1"}],"container-title":["ACM Transactions on Mathematical Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653071","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3653071","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:56:55Z","timestamp":1750291015000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":70,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6,30]]}},"alternative-id":["10.1145\/3653071"],"URL":"https:\/\/doi.org\/10.1145\/3653071","relation":{},"ISSN":["0098-3500","1557-7295"],"issn-type":[{"value":"0098-3500","type":"print"},{"value":"1557-7295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"2023-03-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}