{"id":"https://openalex.org/W6962783930","doi":"https://doi.org/10.18419/opus-13229","title":"Physics-informed neural networks for learning dynamic, distributed and uncertain systems","display_name":"Physics-informed neural networks for learning dynamic, distributed and uncertain systems","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W6962783930","doi":"https://doi.org/10.18419/opus-13229"},"language":"en","primary_location":{"id":"pmh:oai:elib.uni-stuttgart.de:11682/13248","is_oa":true,"landing_page_url":"http://elib.uni-stuttgart.de/handle/11682/13248","pdf_url":null,"source":{"id":"https://openalex.org/S4306401556","display_name":"OPUS Publication Server of the University of Stuttgart (University of Stuttgart)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I100066346","host_organization_name":"University of Stuttgart","host_organization_lineage":["https://openalex.org/I100066346"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralThesis"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://elib.uni-stuttgart.de/handle/11682/13248","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Praditia, Timothy","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Praditia, Timothy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":true,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.8819000124931335,"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":0.8819000124931335,"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":0.03799999877810478,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.014000000432133675,"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/imperfect","display_name":"Imperfect","score":0.5845999717712402},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5708000063896179},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44850000739097595},{"id":"https://openalex.org/keywords/scientific-modelling","display_name":"Scientific modelling","score":0.40220001339912415},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.3928000032901764},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.37310001254081726},{"id":"https://openalex.org/keywords/complex-system","display_name":"Complex system","score":0.337799996137619},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.33629998564720154}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678600013256073},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5708000063896179},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44850000739097595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43470001220703125},{"id":"https://openalex.org/C138379479","wikidata":"https://www.wikidata.org/wiki/Q1116876","display_name":"Scientific modelling","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3853999972343445},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C47822265","wikidata":"https://www.wikidata.org/wiki/Q854457","display_name":"Complex system","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C530175646","wikidata":"https://www.wikidata.org/wiki/Q11460","display_name":"Clothing","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3093000054359436},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.29989999532699585},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:elib.uni-stuttgart.de:11682/13248","is_oa":true,"landing_page_url":"http://elib.uni-stuttgart.de/handle/11682/13248","pdf_url":null,"source":{"id":"https://openalex.org/S4306401556","display_name":"OPUS Publication Server of the University of Stuttgart (University of Stuttgart)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I100066346","host_organization_name":"University of Stuttgart","host_organization_lineage":["https://openalex.org/I100066346"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralThesis"},{"id":"pmh:oai:henry.baw.de:20.500.11970/112770","is_oa":true,"landing_page_url":"https://hdl.handle.net/20.500.11970/112770","pdf_url":null,"source":{"id":"https://openalex.org/S4306402631","display_name":"Hydraulic Engineering Repository (HENRY) (Bundesanstalt f\u00fcr Wasserbau)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210152653","host_organization_name":"Bundesanstalt f\u00fcr Wasserbau","host_organization_lineage":["https://openalex.org/I4210152653"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Doctoral Thesis"},{"id":"doi:10.18419/opus-13229","is_oa":true,"landing_page_url":"https://doi.org/10.18419/opus-13229","pdf_url":null,"source":{"id":"https://openalex.org/S7407052998","display_name":"Universit\u00e4tsbibliothek Stuttgart","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Collection"}],"best_oa_location":{"id":"pmh:oai:elib.uni-stuttgart.de:11682/13248","is_oa":true,"landing_page_url":"http://elib.uni-stuttgart.de/handle/11682/13248","pdf_url":null,"source":{"id":"https://openalex.org/S4306401556","display_name":"OPUS Publication Server of the University of Stuttgart (University of Stuttgart)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I100066346","host_organization_name":"University of Stuttgart","host_organization_lineage":["https://openalex.org/I100066346"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scientific":[0],"models":[1,94,106,156,367,433],"play":[2],"an":[3,83],"important":[4],"role":[5],"in":[6,21,42,74,77,86,98,135,140,240,299,418,439,574],"many":[7],"technical":[8],"inventions":[9],"to":[10,18,61,70,90,96,118,169,192,213,267,269,324,366,483,502,529,551,597,603],"facilitate":[11,255],"daily":[12],"human":[13],"activities.":[14],"We":[15],"use":[16],"them":[17],"assist":[19],"us":[20],"simple":[22],"decision":[23],"making":[24],"such":[25,45,348],"as":[26,46,349],"deciding":[27],"what":[28],"type":[29],"of":[30,51,65,111,147,163,282,302,333,384,403,414,437,460,475,533,586,609],"clothing":[31],"we":[32],"should":[33],"wear":[34],"using":[35],"the":[36,48,71,99,136,145,160,167,230,286,300,326,330,334,338,357,381,394,400,412,426,448,452,458,466,485,488,522,526,531,534,538,554,580,591,604],"weather":[37],"forecast":[38],"model,":[39],"and":[40,95,166,196,208,217,223,329,351,361,441,495,506,546,578,606,614],"also":[41],"complex":[43,66,587],"problems":[44],"assessing":[47],"environmental":[49],"impact":[50],"industrial":[52],"wastes.":[53],"Existing":[54],"scientific":[55,101,256,274,576,611,615],"models,":[56],"however,":[57],"are":[58,182,369,500],"imperfect":[59],"due":[60],"our":[62],"limited":[63],"understanding":[64],"physical":[67,130,188,207,346,372,544],"systems.":[68],"Due":[69],"rapid":[72],"growth":[73],"computing":[75],"power":[76],"recent":[78],"years,":[79],"there":[80],"has":[81,132,172],"been":[82,133,173],"increasing":[84],"interest":[85],"applying":[87],"data-driven":[88,105],"modeling":[89,293,567],"improve":[91],"upon":[92],"current":[93,154],"fill":[97],"missing":[100,575],"knowledge.":[102],"Traditionally,":[103],"these":[104,264,559],"require":[107],"a":[108,122,194,215,241,292,296,303,355,389,470,476,504,513,542,564,583],"significant":[109],"amount":[110],"observation":[112],"data,":[113],"which":[114],"is":[115,248,317,340,378,481,492,510,525,570,593],"often":[116],"challenging":[117],"obtain,":[119],"especially":[120],"from":[121],"natural":[123],"system.":[124,335],"To":[125,175],"address":[126],"this":[127,177,283,385,561,595],"issue,":[128],"prior":[129,187,206],"knowledge":[131,189,210,577,612],"included":[134],"model":[137,199,220,232,316,339,358,391,410,428,540,545,616],"design,":[138],"resulting":[139,409],"so-called":[141],"hybrid":[142,198,219,231,277,390,467,539,566],"models.":[143,278],"Although":[144],"idea":[146],"infusing":[148],"physics":[149],"with":[150,263,312,320,399],"data":[151,171,474,491],"seems":[152],"sound,":[153],"state-of-the-art":[155,430],"have":[157],"not":[158],"found":[159],"ideal":[161],"combination":[162],"both":[164,610],"aspects,":[165],"application":[168],"real-world":[170,251,588],"lacking.":[174],"bridge":[176],"gap,":[178],"three":[179],"research":[180,288,376,454],"questions":[181,265],"formulated:":[183],"1.":[184],"How":[185,204,228],"can":[186,205,229],"be":[190,211],"adopted":[191,212],"design":[193,214],"consistent":[195,216,271],"reliable":[197,218],"for":[200,221,250,273,295,572,582,594],"dynamic":[201,222,297],"systems?":[202,226],"2.":[203],"numerical":[209],"spatially":[224],"distributed":[225],"3.":[227],"learn":[233],"about":[234],"its":[235],"own":[236],"total":[237],"(predictive)":[238],"uncertainty":[239,462,497,532,550],"computationally":[242],"effective":[243],"manner,":[244],"so":[245],"that":[246,368,392,425,512,569,601],"it":[247,343,442],"appropriate":[249,548],"applications":[252],"or":[253],"could":[254],"hypothesis":[257],"testing?":[258],"The":[259,279,374,408],"overall":[260],"goal":[261],"is,":[262],"answered,":[266],"contribute":[268,602],"more":[270],"approaches":[272],"inquiry":[275],"through":[276],"first":[280,287],"contribution":[281,383,450],"thesis":[284,423,562],"addresses":[285,451],"question":[289,377],"by":[290,380,387,434,456],"proposing":[291],"framework":[294,568],"system,":[298],"form":[301],"Thermochemical":[304],"Energy":[305],"Storage":[306],"device.":[307],"A":[308],"Nonlinear":[309],"Autoregressive":[310],"Network":[311],"Exogeneous":[313],"Input":[314],"(NARX)":[315],"trained":[318,370],"recurrently":[319],"multiple":[321],"time":[322],"lags":[323],"capture":[325],"temporal":[327],"dependency":[328],"long-term":[331],"dynamics":[332],"During":[336],"training,":[337],"penalized":[341],"when":[342],"violates":[344],"established":[345],"laws,":[347],"mass":[350],"energy":[352],"conservation.":[353],"As":[354,469],"result,":[356],"produces":[359],"accurate":[360],"physically":[362],"plausible":[363],"predictions":[364],"compared":[365],"without":[371],"regularization.":[373],"second":[375,382],"addressed":[379],"thesis,":[386],"designing":[388],"complements":[393],"Finite":[395],"Volume":[396],"Method":[397],"(FVM)":[398],"learning":[401,413,432],"ability":[402],"Artificial":[404],"Neural":[405],"Networks":[406],"(ANNs).":[407],"enables":[411],"unknown":[415],"closure/constitutive":[416],"relationships":[417],"various":[419],"advection-diffusion":[420],"equations.":[421],"This":[422],"shows":[424],"proposed":[427,535],"outperforms":[429,541],"deep":[431],"several":[435],"orders":[436],"magnitude":[438],"accuracy,":[440],"possesses":[443],"excellent":[444],"generalization":[445],"ability.":[446],"Finally,":[447],"third":[449,453],"question,":[455],"investigating":[457],"performance":[459],"assorted":[461],"quantification":[463,498],"methods":[464,499],"on":[465],"model.":[468,486,508,536],"demonstration,":[471],"laboratory":[472],"measurement":[473,556],"groundwater":[477],"contaminant":[478],"transport":[479],"process":[480],"employed":[482],"train":[484],"Since":[487],"available":[489],"training":[490],"extremely":[493],"scarce":[494],"noisy,":[496],"essential":[501],"produce":[503],"robust":[505,565],"trustworthy":[507],"It":[509],"shown":[511],"gradient-based":[514],"Markov":[515],"Chain":[516],"Monte":[517],"Carlo":[518],"(MCMC)":[519],"algorithm,":[520],"namely":[521],"Barker":[523],"proposal":[524],"most":[527],"suitable":[528,571],"quantify":[530],"Additionally,":[537],"calibrated":[543],"provides":[547],"predictive":[549],"sufficiently":[552],"explain":[553],"noisy":[555],"data.":[557],"With":[558],"contributions,":[560],"proposes":[563],"filling":[573],"lays":[579],"groundwork":[581],"wider":[584],"variety":[585],"applications.":[589],"Ultimately,":[590],"hope":[592],"work":[596],"inspire":[598],"future":[599],"studies":[600],"continuous":[605],"mutual":[607],"improvements":[608],"discovery":[613],"robustness.":[617]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
