{"id":"https://openalex.org/W4402454392","doi":"https://doi.org/10.1145/3674029.3674043","title":"Deep and Physics-Informed Neural Networks as a Substitute for Finite Element Analysis","display_name":"Deep and Physics-Informed Neural Networks as a Substitute for Finite Element Analysis","publication_year":2024,"publication_date":"2024-05-24","ids":{"openalex":"https://openalex.org/W4402454392","doi":"https://doi.org/10.1145/3674029.3674043"},"language":"en","primary_location":{"id":"doi:10.1145/3674029.3674043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3674029.3674043?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3674029.3674043?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001446716","display_name":"Lu\u00eds Santos","orcid":"https://orcid.org/0000-0002-0009-8752"},"institutions":[{"id":"https://openalex.org/I28257850","display_name":"London South Bank University","ror":"https://ror.org/02vwnat91","country_code":"GB","type":"education","lineage":["https://openalex.org/I28257850"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Luis Santos","raw_affiliation_strings":["School of the Built Environment and Architecture, London South Bank University, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-0009-8752","affiliations":[{"raw_affiliation_string":"School of the Built Environment and Architecture, London South Bank University, United Kingdom","institution_ids":["https://openalex.org/I28257850"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5001446716"],"corresponding_institution_ids":["https://openalex.org/I28257850"],"apc_list":null,"apc_paid":null,"fwci":0.807,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.71014821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"84","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9997000098228455,"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":0.9997000098228455,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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":0.9886000156402588,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/finite-element-method","display_name":"Finite element method","score":0.708168625831604},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6065806150436401},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5205696225166321},{"id":"https://openalex.org/keywords/element","display_name":"Element (criminal law)","score":0.42423495650291443},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3697514533996582},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24628260731697083},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.13018569350242615},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.09086617827415466}],"concepts":[{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.708168625831604},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6065806150436401},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5205696225166321},{"id":"https://openalex.org/C200288055","wikidata":"https://www.wikidata.org/wiki/Q2621792","display_name":"Element (criminal law)","level":2,"score":0.42423495650291443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3697514533996582},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24628260731697083},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.13018569350242615},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.09086617827415466},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3674029.3674043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3674029.3674043?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3674029.3674043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3674029.3674043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3674029.3674043?download=true","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 9th International Conference on Machine Learning Technologies (ICMLT)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402454392.pdf","grobid_xml":"https://content.openalex.org/works/W4402454392.grobid-xml"},"referenced_works_count":16,"referenced_works":["https://openalex.org/W2785071288","https://openalex.org/W2899283552","https://openalex.org/W2947047078","https://openalex.org/W3014009018","https://openalex.org/W3036063661","https://openalex.org/W3092020108","https://openalex.org/W3092743582","https://openalex.org/W3127451557","https://openalex.org/W3163993681","https://openalex.org/W3164032021","https://openalex.org/W4213248101","https://openalex.org/W4286797646","https://openalex.org/W4311532834","https://openalex.org/W4313715577","https://openalex.org/W4380988666","https://openalex.org/W4398238760"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Today,":[0],"an":[1,143],"engineer's":[2],"success":[3],"is":[4,147],"largely":[5],"based":[6],"on":[7,103],"their":[8],"skill":[9],"at":[10],"employing":[11],"Finite":[12],"Element":[13],"Analysis":[14],"(FEA),":[15],"a":[16,95,161,166,173],"standard":[17],"engineering":[18,57,144],"modeling":[19],"tool":[20],"to":[21,49,55,199,230],"numerically":[22],"assess":[23],"the":[24,110,128,139,154,195,213,224],"behavior":[25,141],"of":[26,112,131],"structures.":[27],"So":[28],"far,":[29],"improvements":[30],"in":[31,42,136],"computational":[32,233],"power":[33,111],"and":[34,52,87,122,157,178,202],"FEA":[35,45,60,135],"element":[36],"formulations":[37],"have":[38],"supported":[39],"significant":[40],"advancements":[41],"this":[43],"field.":[44],"can":[46,84,151,181],"be":[47,182,192],"used":[48],"provide":[50],"accurate":[51,179],"fast":[53],"answers":[54],"many":[56],"problems.":[58,188],"However,":[59],"models":[61],"are":[62],"still":[63],"associated":[64],"with":[65,127,172,194],"prohibitive":[66],"time":[67],"costs":[68],"when":[69],"solving":[70],"complex":[71],"scenarios.":[72],"Unlike":[73],"traditional":[74,232],"computer":[75],"algorithms,":[76,115],"Machine":[77,113],"Learning,":[78],"by":[79,108],"minimizing":[80],"pre-established":[81],"loss":[82],"functions,":[83],"ascertain":[85],"patterns":[86],"make":[88],"predictions":[89,180],"without":[90],"being":[91,208],"explicitly":[92],"programmed":[93],"for":[94,160,186,226],"given":[96],"task.":[97],"This":[98],"paper":[99],"presents":[100],"initial":[101],"results":[102,222],"improving":[104],"structural":[105,140],"analysis":[106],"tools":[107],"harnessing":[109],"Learning":[114],"namely":[116],"Deep":[117],"Artificial":[118],"Neural":[119,124],"Networks":[120,125],"(ANNs)":[121],"Physics-Informed":[123],"(PINNs),":[126],"final":[129],"objective":[130],"substituting":[132],"or":[133],"accelerating":[134],"accurately":[137,152],"predicting":[138],"within":[142],"domain.":[145],"It":[146],"found":[148],"that":[149],"ANNs":[150,169],"predict":[153],"stress,":[155],"strain,":[156],"displacement":[158],"maps":[159],"cantilevered":[162],"rectangular":[163],"plate":[164],"under":[165],"concentrated":[167],"load.":[168],"demonstrate":[170],"that,":[171,206],"large":[174],"training":[175],"dataset,":[176],"efficient":[177,210],"achieved":[183],"although":[184],"only":[185],"specific":[187],"These":[189],"algorithms":[190,229],"could":[191],"enhanced":[193],"governing":[196],"differential":[197],"equations":[198],"improve":[200],"convergence":[201],"speed.":[203],"PINNs":[204],"show":[205,223],"despite":[207],"less":[209],"than":[211,219],"FEA,":[212],"method":[214],"shows":[215],"more":[216],"generalization":[217],"potential":[218,225],"ANNs.":[220],"Both":[221],"machine":[227],"learning":[228],"enhance":[231],"mechanics":[234],"methods.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2025-10-10T00:00:00"}
