{"id":"https://openalex.org/W4386971196","doi":"https://doi.org/10.1115/omae2023-104748","title":"Deep Learning Based Prediction of Hydrodynamic Forces on Offshore Platforms","display_name":"Deep Learning Based Prediction of Hydrodynamic Forces on Offshore Platforms","publication_year":2023,"publication_date":"2023-06-11","ids":{"openalex":"https://openalex.org/W4386971196","doi":"https://doi.org/10.1115/omae2023-104748"},"language":"en","primary_location":{"id":"doi:10.1115/omae2023-104748","doi":"https://doi.org/10.1115/omae2023-104748","is_oa":false,"landing_page_url":"https://doi.org/10.1115/omae2023-104748","pdf_url":null,"source":{"id":"https://openalex.org/S4306499009","display_name":"International Conference on Offshore Mechanics and Arctic Engineering (OMAE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Volume 7: CFD & FSI","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5046292441","display_name":"T. P. Miyanawala","orcid":"https://orcid.org/0000-0002-2484-8896","observed_orcids":["https://orcid.org/0000-0002-2484-8896"]},"institutions":[{"id":"https://openalex.org/I4210116904","display_name":"Technology Centre for Offshore and Marine, Singapore","ror":"https://ror.org/02gqf9e56","country_code":"SG","type":"facility","lineage":["https://openalex.org/I4210116904"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tharindu Pradeeptha Miyanawala","raw_affiliation_strings":["Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore","institution_ids":["https://openalex.org/I4210116904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404095","display_name":"Yulong Li","orcid":"https://orcid.org/0000-0002-4219-9041","observed_orcids":["https://orcid.org/0000-0002-4219-9041","https://orcid.org/0000-0001-9899-483X","https://orcid.org/0009-0007-5331-2621"]},"institutions":[{"id":"https://openalex.org/I4210116904","display_name":"Technology Centre for Offshore and Marine, Singapore","ror":"https://ror.org/02gqf9e56","country_code":"SG","type":"facility","lineage":["https://openalex.org/I4210116904"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yulong Li","raw_affiliation_strings":["Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore","institution_ids":["https://openalex.org/I4210116904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047771507","display_name":"Yun Zhi Law","orcid":"https://orcid.org/0000-0002-1476-7435","observed_orcids":["https://orcid.org/0000-0002-1476-7435"]},"institutions":[{"id":"https://openalex.org/I4210116904","display_name":"Technology Centre for Offshore and Marine, Singapore","ror":"https://ror.org/02gqf9e56","country_code":"SG","type":"facility","lineage":["https://openalex.org/I4210116904"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yun Zhi Law","raw_affiliation_strings":["Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore","institution_ids":["https://openalex.org/I4210116904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026663470","display_name":"Harrif Santo","orcid":"https://orcid.org/0000-0001-9041-1126","observed_orcids":["https://orcid.org/0000-0001-9041-1126"]},"institutions":[{"id":"https://openalex.org/I4210116904","display_name":"Technology Centre for Offshore and Marine, Singapore","ror":"https://ror.org/02gqf9e56","country_code":"SG","type":"facility","lineage":["https://openalex.org/I4210116904"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Harrif Santo","raw_affiliation_strings":["Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technology Centre for Offshore and Marine, Singapore (TCOMS) , Singapore, Singapore","institution_ids":["https://openalex.org/I4210116904"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210116904"],"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":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":0.9365000128746033,"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":0.9365000128746033,"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/T11250","display_name":"Wave and Wind Energy Systems","score":0.023099999874830246,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11694","display_name":"Fluid Dynamics Simulations and Interactions","score":0.0066999997943639755,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/drag-force","display_name":"drag force","score":0.621999979019165},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.4830000102519989},{"id":"https://openalex.org/keywords/deep-learning-surrogate-model","display_name":"deep learning surrogate model","score":0.5609999895095825},{"id":"https://openalex.org/keywords/jonswap-spectrum","display_name":"JONSWAP spectrum","score":0.8759999871253967},{"id":"https://openalex.org/keywords/slender-bodies","display_name":"slender bodies","score":0.6940000057220459},{"id":"https://openalex.org/keywords/digital-twin","display_name":"digital twin","score":0.6190000176429749},{"id":"https://openalex.org/keywords/parametric-design","display_name":"parametric design","score":0.7870000004768372}],"concepts":[{"id":"https://openalex.org/C72921944","wikidata":"https://www.wikidata.org/wiki/Q206621","display_name":"Drag","level":2,"score":0.6900461316108704},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6502475738525391},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6277865171432495},{"id":"https://openalex.org/C110407247","wikidata":"https://www.wikidata.org/wiki/Q122508","display_name":"Inertia","level":2,"score":0.5783604979515076},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5472984313964844},{"id":"https://openalex.org/C162284963","wikidata":"https://www.wikidata.org/wiki/Q17106102","display_name":"Submarine pipeline","level":2,"score":0.49498289823532104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43349123001098633},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.41498011350631714},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4135032892227173},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40729957818984985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3587592840194702},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.3121195435523987},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2496648132801056},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20885556936264038},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2069571316242218},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.17774760723114014},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1115/omae2023-104748","doi":"https://doi.org/10.1115/omae2023-104748","is_oa":false,"landing_page_url":"https://doi.org/10.1115/omae2023-104748","pdf_url":null,"source":{"id":"https://openalex.org/S4306499009","display_name":"International Conference on Offshore Mechanics and Arctic Engineering (OMAE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Volume 7: CFD & FSI","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4396850338","https://openalex.org/W4413391779","https://openalex.org/W3086770284","https://openalex.org/W7214642920","https://openalex.org/W2892709038","https://openalex.org/W7213275065","https://openalex.org/W4368339570","https://openalex.org/W7155032407","https://openalex.org/W4386222276","https://openalex.org/W4399270179"],"abstract_inverted_index":{"Abstract":[0],"Irregular":[1],"ocean":[2],"waves":[3,103],"cause":[4],"fluctuating":[5],"non-linear":[6,76],"hydrodynamic":[7,60,90,143],"forces":[8,61,67,144],"on":[9,62,105,145,164],"the":[10,82,109,112,117,142,146,149,154,158,161,180,192,198],"body,":[11],"which":[12],"is":[13,78,99,129],"an":[14,47,185],"importance":[15],"factor":[16],"to":[17,57,80,137,179,200],"account":[18],"for":[19,41,203],"in":[20],"offshore":[21,124],"and":[22,28,39,89,111,148,183,206],"marine":[23],"applications.":[24],"While":[25],"physical":[26],"experimental":[27],"computational":[29,189],"techniques":[30],"provide":[31],"valuable":[32],"physics":[33],"insight,":[34],"they":[35],"are":[36,68,151],"generally":[37],"time-consuming":[38],"expensive":[40],"design":[42,205],"space":[43],"exploration.":[44],"We":[45],"present":[46],"efficient":[48],"Long":[49],"Short":[50],"Time":[51],"Memory":[52],"(LSTM)":[53],"based":[54,104,163],"deep-learning":[55],"technique":[56],"predict":[58,138],"unsteady":[59],"slender":[63],"bodies,":[64],"where":[65],"drag":[66],"more":[69],"dominant":[70],"than":[71],"inertia":[72],"forces.":[73],"A":[74],"leaky":[75],"rectification":[77],"employed":[79],"approximate":[81],"mapping":[83],"between":[84],"surface":[85],"elevation":[86],"time":[87,93,139],"series":[88,140],"(drag)":[91],"force":[92],"series.":[94],"The":[95,127],"deep":[96,166],"neural":[97],"network":[98,168],"fed":[100],"with":[101,153],"irregular":[102],"JONSWAP":[106],"spectrum":[107],"as":[108],"input":[110],"target":[113],"data":[114],"generated":[115],"by":[116],"full-order":[118,155,181],"Navier-Stokes":[119],"computations":[120],"using":[121,131],"a":[122,132,170],"generic":[123],"jack-up":[125,147,210],"platform.":[126],"LSTM":[128],"trained":[130],"stochastic":[133],"gradient":[134],"descent":[135],"method":[136],"of":[141,172,176,188,209],"results":[150,182],"compared":[152,178],"computations.":[156],"Within":[157],"error":[159],"threshold,":[160],"predictions":[162],"our":[165],"convolutional":[167],"have":[169],"speed-up":[171],"nearly":[173],"2":[174],"orders":[175],"magnitude":[177],"consume":[184],"insignificant":[186],"fraction":[187],"resources.":[190],"Overall,":[191],"proposed":[193],"LSTM-based":[194],"approximation":[195],"procedure":[196],"has":[197],"potential":[199],"be":[201],"used":[202],"parametric":[204],"digital":[207],"twinning":[208],"platforms.":[211]},"counts_by_year":[],"updated_date":"2026-11-01T11:40:01.658920","created_date":"2023-09-23T00:00:00"}