{"id":"https://openalex.org/W7204183655","doi":"https://doi.org/10.1145/3817395.3817426","title":"Inverse Design Method for Helmholtz Resonant Acoustic Absorbers Based on Physics-Informed Neural Networks","display_name":"Inverse Design Method for Helmholtz Resonant Acoustic Absorbers Based on Physics-Informed Neural Networks","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7204183655","doi":"https://doi.org/10.1145/3817395.3817426"},"language":"en","primary_location":{"id":"doi:10.1145/3817395.3817426","doi":"https://doi.org/10.1145/3817395.3817426","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3817395.3817426","pdf_url":null,"source":{"id":"https://openalex.org/S7407115342","display_name":"International Conference on Big Data and Intelligent Manufacturing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Big Data and Intelligent Manufacturing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3817395.3817426","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148165477","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0005-0573-6230","observed_orcids":["https://orcid.org/0009-0005-0573-6230"]},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["School of mechanical engineering, Beijing institute of technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-0573-6230","affiliations":[{"raw_affiliation_string":"School of mechanical engineering, Beijing institute of technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070115551","display_name":"Shikai Jing","orcid":"https://orcid.org/0000-0001-7445-3051","observed_orcids":["https://orcid.org/0000-0001-7445-3051","https://orcid.org/0009-0001-8038-8839"]},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shikai Jing","raw_affiliation_strings":["School of mechanical engineering, Beijing institute of technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0001-8038-8839","affiliations":[{"raw_affiliation_string":"School of mechanical engineering, Beijing institute of technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"206","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9672999978065491,"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":0.9672999978065491,"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/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.026200000196695328,"subfield":{"id":"https://openalex.org/subfields/3102","display_name":"Acoustics and Ultrasonics"},"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/T10320","display_name":"Neural Networks and Applications","score":0.0020000000949949026,"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/inverse-design","display_name":"inverse design","score":0.9079999923706055},{"id":"https://openalex.org/keywords/helmholtz-resonator","display_name":"Helmholtz resonator","score":0.9259999990463257},{"id":"https://openalex.org/keywords/acoustic-absorbers","display_name":"acoustic absorbers","score":0.9620000123977661},{"id":"https://openalex.org/keywords/physics-informed-neural-networks","display_name":"physics-informed neural networks","score":0.9589999914169312},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.7360000014305115},{"id":"https://openalex.org/keywords/low-frequency-sound-absorption","display_name":"low-frequency sound absorption","score":0.8180000185966492},{"id":"https://openalex.org/keywords/underwater-acoustics","display_name":"underwater acoustics","score":0.8069999814033508},{"id":"https://openalex.org/keywords/parametric-modeling","display_name":"parametric modeling","score":0.6909999847412109},{"id":"https://openalex.org/keywords/latent-feature-extraction","display_name":"latent feature extraction","score":0.6570000052452087}],"concepts":[{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.63919997215271},{"id":"https://openalex.org/C27592594","wikidata":"https://www.wikidata.org/wiki/Q865821","display_name":"Helmholtz free energy","level":2,"score":0.597100019454956},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5918999910354614},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5299000144004822},{"id":"https://openalex.org/C24890656","wikidata":"https://www.wikidata.org/wiki/Q82811","display_name":"Acoustics","level":1,"score":0.492900013923645},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C125287762","wikidata":"https://www.wikidata.org/wiki/Q1758948","display_name":"Absorption (acoustics)","level":2,"score":0.48260000348091125},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.45989999175071716},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.396699994802475},{"id":"https://openalex.org/C60015446","wikidata":"https://www.wikidata.org/wiki/Q7624999","display_name":"Structural acoustics","level":3,"score":0.38830000162124634},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3767000138759613},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.3702999949455261},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3614000082015991},{"id":"https://openalex.org/C18591234","wikidata":"https://www.wikidata.org/wiki/Q860615","display_name":"Helmholtz equation","level":3,"score":0.3546000123023987},{"id":"https://openalex.org/C170344590","wikidata":"https://www.wikidata.org/wiki/Q1569889","display_name":"Helmholtz resonator","level":3,"score":0.3255000114440918},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C2983319462","wikidata":"https://www.wikidata.org/wiki/Q15948129","display_name":"Inverse method","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2964000105857849},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C155635449","wikidata":"https://www.wikidata.org/wiki/Q4674699","display_name":"Acoustic model","level":3,"score":0.2799000144004822},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3817395.3817426","doi":"https://doi.org/10.1145/3817395.3817426","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3817395.3817426","pdf_url":null,"source":{"id":"https://openalex.org/S7407115342","display_name":"International Conference on Big Data and Intelligent Manufacturing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Big Data and Intelligent Manufacturing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3817395.3817426","doi":"https://doi.org/10.1145/3817395.3817426","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3817395.3817426","pdf_url":null,"source":{"id":"https://openalex.org/S7407115342","display_name":"International Conference on Big Data and Intelligent Manufacturing","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 International Conference on Big Data and Intelligent Manufacturing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2989226760","https://openalex.org/W3128157635","https://openalex.org/W3158710982","https://openalex.org/W4321236373","https://openalex.org/W4379191932","https://openalex.org/W4408739705","https://openalex.org/W4414050850","https://openalex.org/W4415230577","https://openalex.org/W4417118021"],"related_works":["https://openalex.org/W4321236373","https://openalex.org/W3158710982","https://openalex.org/W7204861086","https://openalex.org/W3213016690","https://openalex.org/W7134854332","https://openalex.org/W7135206299","https://openalex.org/W7163651006","https://openalex.org/W7084105683","https://openalex.org/W7171566861","https://openalex.org/W4403628061"],"abstract_inverted_index":{"Efficient":[0],"inverse":[1,42,103],"design":[2,43],"of":[3,68,187],"low-frequency":[4],"acoustic":[5,12,48,56,116],"absorption":[6,72,93,107,163],"structures":[7],"is":[8,75,84],"important":[9],"for":[10,45],"underwater":[11],"applications.":[13],"Traditional":[14],"methods":[15],"often":[16],"rely":[17],"on":[18,96],"repeated":[19],"forward":[20],"calculations":[21],"or":[22],"purely":[23],"data-driven":[24],"trial-and-error":[25],"processes,":[26],"which":[27],"are":[28,171],"time-consuming":[29],"and":[30,60,71,132,151,157,167,177],"lack":[31],"physical":[32],"reliability.":[33],"To":[34],"overcome":[35],"this":[36,38],"problem,":[37],"paper":[39],"proposes":[40],"an":[41,54,58,82],"framework":[44],"Helmholtz":[46],"resonant":[47],"absorber":[49],"by":[50,112,154,174],"integrating":[51],"parametric":[52],"modeling,":[53],"analytical":[55,115],"model,":[57],"autoencoder,":[59],"a":[61,134],"physics-informed":[62],"neural":[63],"network":[64],"(PINN).":[65],"A":[66],"dataset":[67],"structural":[69,110],"parameters":[70,111],"coefficient":[73],"curves":[74,108],"first":[76],"constructed":[77],"under":[78],"geometric":[79,147],"constraints.":[80],"Then,":[81],"autoencoder":[83],"used":[85],"to":[86,109],"extract":[87],"low-dimensional":[88],"latent":[89],"features":[90],"from":[91,105],"high-dimensional":[92],"curves.":[94],"Based":[95],"these":[97],"features,":[98],"the":[99,102,114,119,125,142,146,161,185,188],"PINN":[100,144],"establishes":[101],"mapping":[104],"target":[106],"embedding":[113],"model":[117],"into":[118],"training":[120],"process.":[121],"Results":[122],"show":[123],"that":[124],"proposed":[126,143],"method":[127],"achieves":[128],"accurate":[129],"curve":[130,164],"reconstruction":[131],"outperforms":[133],"conventional":[135],"multilayer":[136],"perceptron":[137],"(MLP).":[138],"Compared":[139],"with":[140],"MLP,":[141],"reduces":[145],"parameter":[148],"MAE,":[149,165],"RMSE,":[150,166],"relative":[152],"error":[153,170],"26.28%,":[155],"19.60%,":[156],"17.70%,":[158],"respectively,":[159],"while":[160],"sound":[162],"average":[168],"maximum":[169],"further":[172,183],"reduced":[173],"14.04%,":[175],"6.32%,":[176],"36.18%,":[178],"respectively.":[179],"Finite":[180],"element":[181],"simulations":[182],"verify":[184],"feasibility":[186],"predicted":[189],"structures.":[190]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-08-26T00:00:00"}
