{"id":"https://openalex.org/W7117471014","doi":"https://doi.org/10.1109/dicta68720.2025.11302417","title":"Physics-Informed Operator Learning for Hemodynamic Modeling","display_name":"Physics-Informed Operator Learning for Hemodynamic Modeling","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7117471014","doi":"https://doi.org/10.1109/dicta68720.2025.11302417"},"language":null,"primary_location":{"id":"doi:10.1109/dicta68720.2025.11302417","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302417","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-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/A5120025649","display_name":"Ryan Chappell","orcid":null},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Ryan Chappell","raw_affiliation_strings":["School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043976016","display_name":"Chayan Banerjee","orcid":"https://orcid.org/0000-0003-1039-3744"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Chayan Banerjee","raw_affiliation_strings":["School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021261421","display_name":"Kien T. Nguyen","orcid":"https://orcid.org/0000-0003-2685-3928"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kien Nguyen","raw_affiliation_strings":["School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034095159","display_name":"Clinton Fookes","orcid":"https://orcid.org/0000-0002-8515-6324"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Clinton Fookes","raw_affiliation_strings":["School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia"],"affiliations":[{"raw_affiliation_string":"School of Computing and Electrical Engineering, Queensland University of Technology,Brisbane,Australia","institution_ids":["https://openalex.org/I160993911"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5120025649"],"corresponding_institution_ids":["https://openalex.org/I160993911"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71941242,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.6026999950408936,"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":0.6026999950408936,"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/T10217","display_name":"Cardiac electrophysiology and arrhythmias","score":0.11320000141859055,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.017899999395012856,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5774999856948853},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5156999826431274},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4406999945640564},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4375999867916107},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3222000002861023},{"id":"https://openalex.org/keywords/supervisor","display_name":"Supervisor","score":0.3027999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.666700005531311},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5774999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5519000291824341},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48019999265670776},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4375999867916107},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C2779110517","wikidata":"https://www.wikidata.org/wiki/Q1240788","display_name":"Supervisor","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta68720.2025.11302417","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta68720.2025.11302417","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2973886134","https://openalex.org/W2979786244","https://openalex.org/W3170733857","https://openalex.org/W3186608048","https://openalex.org/W4210349282","https://openalex.org/W4281766451","https://openalex.org/W4319347409","https://openalex.org/W4377286955","https://openalex.org/W4392004327","https://openalex.org/W4396652962","https://openalex.org/W4397008118","https://openalex.org/W4398222301","https://openalex.org/W4401221079","https://openalex.org/W4402100280","https://openalex.org/W4404538860","https://openalex.org/W4410361159","https://openalex.org/W4410876571"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"modeling":[1,206],"of":[2,129],"personalized":[3],"cardiovascular":[4],"dynamics":[5],"is":[6],"crucial":[7],"for":[8,61,139],"non-invasive":[9],"monitoring":[10],"and":[11,42,47,118,135,176,201],"therapy":[12],"planning.":[13],"State-of-the-art":[14],"physics-informed":[15,72,130],"neural":[16,53],"network":[17],"(PINN)":[18],"approaches":[19],"employ":[20],"deep,":[21],"multi-branch":[22],"architectures":[23,64],"with":[24,151,207],"adversarial":[25,117],"or":[26],"contrastive":[27,119],"objectives":[28],"to":[29,81,89,171,204],"enforce":[30],"partial":[31],"differential":[32],"equation":[33],"constraints.":[34,96],"While":[35],"effective,":[36],"these":[37],"enhancements":[38],"introduce":[39],"significant":[40],"training":[41,62,178,195],"implementation":[43,209],"complexity,":[44],"limiting":[45],"scalability":[46],"practical":[48],"deployment.":[49],"We":[50,125],"investigate":[51],"physicsinformed":[52],"operator":[54,83,99,133],"learning":[55,120,134],"models":[56,113],"as":[57,101],"efficient":[58],"supervisory":[59,140],"signals":[60,92],"simplified":[63],"through":[65],"knowledge":[66],"distillation.":[67],"Our":[68,185],"approach":[69,147,203],"pre-trains":[70],"a":[71,102,106,172,198],"DeepONet":[73],"(PI-DeepONet)":[74],"on":[75],"high-fidelity":[76],"cuffless":[77],"blood":[78],"pressure":[79,91],"recordings":[80],"learn":[82],"mappings":[84],"from":[85,167],"raw":[86],"wearable":[87],"waveforms":[88],"beat-to-beat":[90],"under":[93],"embedded":[94],"physics":[95],"This":[97],"pre-trained":[98],"serves":[100],"frozen":[103],"supervisor":[104],"in":[105,132],"lightweight":[107],"knowledge-distillation":[108],"pipeline,":[109],"guiding":[110],"streamlined":[111],"base":[112],"that":[114,188],"eliminate":[115],"complex":[116,152],"components":[121],"while":[122,162],"maintaining":[123],"performance.":[124],"characterize":[126],"the":[127],"role":[128],"regularization":[131,174],"demonstrate":[136,187],"its":[137],"effectiveness":[138],"guidance.":[141],"Through":[142],"extensive":[143],"experiments,":[144],"our":[145],"operatorsupervised":[146],"achieves":[148],"performance":[149],"parity":[150],"baselines":[153],"(correlation:":[154],"0.766":[155],"vs.":[156,160],"0.770,":[157],"RMSE:":[158],"4.452":[159],"4.501),":[161],"dramatically":[163],"reducing":[164],"architectural":[165],"complexity":[166],"eight":[168],"critical":[169],"hyperparameters":[170],"single":[173],"coefficient":[175],"decreasing":[177],"overhead":[179],"by":[180],"<tex":[181],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[182],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{4":[183],"\\%}$</tex>.":[184],"results":[186],"operator-based":[189],"supervision":[190],"effectively":[191],"replaces":[192],"intricate":[193],"multi-component":[194],"strategies,":[196],"offering":[197],"more":[199],"scalable":[200],"interpretable":[202],"physiological":[205],"reduced":[208],"burden.":[210]},"counts_by_year":[],"updated_date":"2025-12-30T23:08:21.542490","created_date":"2025-12-29T00:00:00"}