{"id":"https://openalex.org/W7106326869","doi":"https://doi.org/10.48550/arxiv.2511.15543","title":"A Physics Informed Machine Learning Framework for Optimal Sensor Placement and Parameter Estimation","display_name":"A Physics Informed Machine Learning Framework for Optimal Sensor Placement and Parameter Estimation","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W7106326869","doi":"https://doi.org/10.48550/arxiv.2511.15543"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2511.15543","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.15543","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2511.15543","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Venianakis, Georgios","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Venianakis, Georgios","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Theodoropoulos, Constantinos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Theodoropoulos, Constantinos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Kavousanakis, Michail","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kavousanakis, Michail","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.477400004863739,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.477400004863739,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.3637999892234802,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.04569999873638153,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.6365000009536743},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5564000010490417},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5277000069618225},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5022000074386597},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47690001130104065},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.46709999442100525},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.460999995470047},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.45649999380111694},{"id":"https://openalex.org/keywords/parameter-space","display_name":"Parameter space","score":0.44760000705718994}],"concepts":[{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.6365000009536743},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5713000297546387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5701000094413757},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5564000010490417},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5289999842643738},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5022000074386597},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47690001130104065},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.460999995470047},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.39250001311302185},{"id":"https://openalex.org/C2983447341","wikidata":"https://www.wikidata.org/wiki/Q1413083","display_name":"Model parameter","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C29406490","wikidata":"https://www.wikidata.org/wiki/Q1420659","display_name":"Fisher information","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C2779377595","wikidata":"https://www.wikidata.org/wiki/Q21045424","display_name":"Approximate Bayesian computation","level":3,"score":0.34470000863075256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3287999927997589},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31940001249313354},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31029999256134033},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2971999943256378},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2511.15543","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.15543","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2511.15543","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.15543","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Parameter":[0],"estimation":[1],"remains":[2],"a":[3,64,122,139],"challenging":[4],"task":[5],"across":[6],"many":[7],"areas":[8],"of":[9,37,48,82,85,95,146,158,187],"engineering.":[10],"Because":[11],"data":[12],"acquisition":[13],"can":[14],"often":[15],"be":[16],"costly,":[17],"limited,":[18],"or":[19,77,208],"prone":[20],"to":[21,28,107,168,202],"inaccuracies":[22],"(noise,":[23],"uncertainty)":[24],"it":[25],"is":[26,179],"crucial":[27],"identify":[29],"sensor":[30,113,130,171,211],"configurations":[31],"that":[32,126,193],"provide":[33],"the":[34,40,46,83,92,144,155,174],"maximum":[35],"amount":[36],"information":[38],"about":[39],"unknown":[41],"parameters,":[42],"in":[43,73,142],"particular":[44],"for":[45,69,97],"case":[47],"distributed-parameter":[49,184],"systems,":[50],"where":[51],"spatial":[52],"variations":[53],"are":[54,148,165],"important.":[55],"Physics-Informed":[56],"Neural":[57],"Networks":[58],"(PINNs)":[59],"have":[60],"recently":[61],"emerged":[62],"as":[63,150],"powerful":[65],"machine-learning":[66],"(ML)":[67],"tool":[68],"parameter":[70,133,203],"estimation,":[71],"particularly":[72],"cases":[74],"with":[75],"sparse":[76],"noisy":[78],"measurements,":[79],"overcoming":[80],"some":[81],"limitations":[84],"traditional":[86],"optimization-based":[87],"and":[88,132],"Bayesian":[89],"approaches.":[90],"Despite":[91],"widespread":[93],"use":[94],"PINNs":[96],"solving":[98],"inverse":[99],"problems,":[100],"relatively":[101],"little":[102],"attention":[103],"has":[104],"been":[105],"given":[106],"how":[108],"their":[109],"performance":[110],"depends":[111],"on":[112,181],"placement.":[114],"This":[115,153],"study":[116],"addresses":[117],"this":[118],"gap":[119],"by":[120],"introducing":[121],"comprehensive":[123],"PINN-based":[124],"framework":[125,178],"simultaneously":[127],"tackles":[128],"optimal":[129,170],"placement":[131],"estimation.":[134],"Our":[135],"approach":[136],"involves":[137],"training":[138],"PINN":[140],"model":[141],"which":[143,164],"parameters":[145],"interest":[147],"included":[149],"additional":[151],"inputs.":[152],"enables":[154],"efficient":[156],"computation":[157],"sensitivity":[159],"functions":[160],"through":[161],"automatic":[162],"differentiation,":[163],"then":[166],"used":[167],"determine":[169],"locations":[172],"exploiting":[173],"D-optimality":[175],"criterion.":[176],"The":[177,190],"validated":[180],"two":[182],"illustrative":[183],"reaction-diffusion-advection":[185],"problems":[186],"increasing":[188],"complexity.":[189],"results":[191],"demonstrate":[192],"our":[194],"PINNs-based":[195],"methodology":[196],"consistently":[197],"achieves":[198],"higher":[199],"accuracy":[200],"compared":[201],"values":[204],"estimated":[205],"from":[206],"intuitively":[207],"randomly":[209],"selected":[210],"positions.":[212]},"counts_by_year":[],"updated_date":"2025-11-23T05:13:22.807545","created_date":"2025-11-23T00:00:00"}
