{"id":"https://openalex.org/W2884751849","doi":"https://doi.org/10.1137/17m1143733","title":"Optimal Experimental Design for Inverse Problems with State Constraints","display_name":"Optimal Experimental Design for Inverse Problems with State Constraints","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2884751849","doi":"https://doi.org/10.1137/17m1143733","mag":"2884751849"},"language":"en","primary_location":{"id":"doi:10.1137/17m1143733","is_oa":false,"landing_page_url":"https://doi.org/10.1137/17m1143733","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Scientific Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076591073","display_name":"Lars Ruthotto","orcid":"https://orcid.org/0000-0003-0803-3299"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lars Ruthotto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004996901","display_name":"Julianne Chung","orcid":"https://orcid.org/0000-0002-6760-4736"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Julianne Chung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5054213578","display_name":"Matthias Chung","orcid":"https://orcid.org/0000-0001-7822-4539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthias Chung","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076591073"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2295,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.86976072,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"40","issue":"4","first_page":"B1080","last_page":"B1100"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11205","display_name":"Numerical methods in inverse problems","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.7407130002975464},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.6002339124679565},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5256337523460388},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5244638919830322},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.502622127532959},{"id":"https://openalex.org/keywords/interior-point-method","display_name":"Interior point method","score":0.4804501235485077},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4784981310367584},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.439511239528656},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.42516791820526123},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4204803705215454},{"id":"https://openalex.org/keywords/optimal-design","display_name":"Optimal design","score":0.417985200881958},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.4107885956764221},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2893669903278351},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.27377480268478394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11692896485328674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.08429399132728577}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.7407130002975464},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.6002339124679565},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5256337523460388},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5244638919830322},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.502622127532959},{"id":"https://openalex.org/C155253501","wikidata":"https://www.wikidata.org/wiki/Q461992","display_name":"Interior point method","level":2,"score":0.4804501235485077},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4784981310367584},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.439511239528656},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.42516791820526123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4204803705215454},{"id":"https://openalex.org/C186394612","wikidata":"https://www.wikidata.org/wiki/Q7098942","display_name":"Optimal design","level":2,"score":0.417985200881958},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.4107885956764221},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2893669903278351},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27377480268478394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11692896485328674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.08429399132728577},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1137/17m1143733","is_oa":false,"landing_page_url":"https://doi.org/10.1137/17m1143733","pdf_url":null,"source":{"id":"https://openalex.org/S165512578","display_name":"SIAM Journal on Scientific Computing","issn_l":"1064-8275","issn":["1064-8275","1095-7197"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SIAM Journal on Scientific Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6243801703","display_name":null,"funder_award_id":"DMS 1522599","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7441234451","display_name":null,"funder_award_id":"DMS 1723005","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7596748135","display_name":null,"funder_award_id":"DMS 1654175","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8280225390","display_name":null,"funder_award_id":"DMS-1127914","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W340244495","https://openalex.org/W1494853941","https://openalex.org/W1541527977","https://openalex.org/W1583562684","https://openalex.org/W1602410243","https://openalex.org/W1741578612","https://openalex.org/W1981265672","https://openalex.org/W1985666665","https://openalex.org/W1987976208","https://openalex.org/W1994613299","https://openalex.org/W2005681075","https://openalex.org/W2014018052","https://openalex.org/W2019143734","https://openalex.org/W2028105432","https://openalex.org/W2033106066","https://openalex.org/W2037521346","https://openalex.org/W2039472483","https://openalex.org/W2054214769","https://openalex.org/W2057424870","https://openalex.org/W2058583833","https://openalex.org/W2129131372","https://openalex.org/W2142716215","https://openalex.org/W2156195892","https://openalex.org/W2157186153","https://openalex.org/W2162619464","https://openalex.org/W2164452299","https://openalex.org/W2594594773","https://openalex.org/W2619943112","https://openalex.org/W2914660408","https://openalex.org/W2963427446","https://openalex.org/W2964244196","https://openalex.org/W2964324126","https://openalex.org/W4242379934"],"related_works":["https://openalex.org/W4378529241","https://openalex.org/W2483828597","https://openalex.org/W1991093342","https://openalex.org/W2094757704","https://openalex.org/W2088213153","https://openalex.org/W2064842217","https://openalex.org/W2071063111","https://openalex.org/W2154951198","https://openalex.org/W25030584","https://openalex.org/W2078622645"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,102,176,193],"address":[4,184],"the":[5,29,34,46,64,97,104,109,117,131,152,164,185,195,198,217,222,227],"challenging":[6],"problem":[7,111,133,158],"of":[8,13,36,45,130,166,189,219,229],"optimal":[9,68,223],"experimental":[10,30],"design":[11,47,65,224],"(OED)":[12],"inverse":[14,99,132],"problems":[15,82],"with":[16],"state":[17,100],"constraints.":[18],"We":[19,79],"consider":[20,80],"two":[21],"OED":[22,119,230],"formulations":[23],"that":[24],"allow":[25],"us":[26,137],"to":[27,55,112,138],"reduce":[28],"costs":[31],"by":[32,73],"minimizing":[33],"number":[35],"measurements.":[37],"The":[38,60,121],"first":[39],"formulation":[40,62,123],"assumes":[41],"a":[42,75,84,178],"fine":[43],"discretization":[44],"parameter":[48],"space":[49],"and":[50,66,134,203,212,225],"uses":[51],"sparsity":[52],"promoting":[53],"regularization":[54],"obtain":[56],"an":[57,90,127,146],"efficient":[58,147],"design.":[59],"second":[61],"parameterizes":[63],"seeks":[67],"placement":[69],"for":[70,108,116,150,172,208,231],"these":[71],"measurements":[72],"solving":[74,151],"small-dimensional":[76],"optimization":[77,148,157],"problem.":[78,120],"both":[81],"in":[83,168],"Bayes":[85,92,191],"risk":[86,93],"as":[87,89],"well":[88],"empirical":[91,122,190],"minimization":[94],"framework.":[95],"For":[96],"unconstrained":[98],"problem,":[101],"exploit":[103],"closed":[105],"form":[106],"solution":[107,129],"inner":[110],"efficiently":[113],"compute":[114],"derivatives":[115],"outer":[118],"does":[124],"not":[125],"require":[126],"explicit":[128],"therefore":[135],"allows":[136],"integrate":[139],"constraints":[140,220],"efficiently.":[141],"A":[142],"key":[143],"contribution":[144],"is":[145],"method":[149],"resulting,":[153],"typically":[154],"high-dimensional,":[155],"bilevel":[156],"using":[159],"derivative-based":[160],"methods.":[161],"To":[162,183],"overcome":[163],"lack":[165],"nondifferentiability":[167],"active":[169],"set":[170],"methods":[171],"inequality":[173],"constrained":[174,232],"problems,":[175],"use":[177],"relaxed":[179],"interior":[180],"point":[181],"method.":[182],"growing":[186],"computational":[187],"complexity":[188],"OED,":[192],"parallelize":[194],"computation":[196],"over":[197],"training":[199],"models.":[200],"Numerical":[201],"examples":[202],"illustrations":[204],"from":[205],"tomographic":[206],"reconstruction,":[207],"various":[209],"data":[210],"sets":[211],"under":[213],"different":[214],"constraints,":[215],"demonstrate":[216],"impact":[218],"on":[221],"highlight":[226],"importance":[228],"problems.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
