{"id":"https://openalex.org/W4402259522","doi":"https://doi.org/10.1109/igarss53475.2024.10642139","title":"Graph Neural Network as Computationally Efficient Emulator Of Ice-Sheet And Sea-Level System Model (ISSM)","display_name":"Graph Neural Network as Computationally Efficient Emulator Of Ice-Sheet And Sea-Level System Model (ISSM)","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402259522","doi":"https://doi.org/10.1109/igarss53475.2024.10642139"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10642139","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10642139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5090267444","display_name":"Younghyun Koo","orcid":"https://orcid.org/0000-0001-9235-5009"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Younghyun Koo","raw_affiliation_strings":["Lehigh University,Department of Computer Science and Engineering, Department of Civil and Environmental Engineering,Bethlehem,PA,USA,18015"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lehigh University,Department of Computer Science and Engineering, Department of Civil and Environmental Engineering,Bethlehem,PA,USA,18015","institution_ids":["https://openalex.org/I186143895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010792548","display_name":"Maryam Rahnemoonfar","orcid":"https://orcid.org/0000-0001-9358-2836"},"institutions":[{"id":"https://openalex.org/I186143895","display_name":"Lehigh University","ror":"https://ror.org/012afjb06","country_code":"US","type":"education","lineage":["https://openalex.org/I186143895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maryam Rahnemoonfar","raw_affiliation_strings":["Lehigh University,Department of Computer Science and Engineering, Department of Civil and Environmental Engineering,Bethlehem,PA,USA,18015"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lehigh University,Department of Computer Science and Engineering, Department of Civil and Environmental Engineering,Bethlehem,PA,USA,18015","institution_ids":["https://openalex.org/I186143895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I186143895"],"apc_list":null,"apc_paid":null,"fwci":0.3556,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54003783,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"32","last_page":"36"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9782999753952026,"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"}},"topics":[{"id":"https://openalex.org/T13650","display_name":"Computational Physics and Python Applications","score":0.9782999753952026,"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"}},{"id":"https://openalex.org/T10644","display_name":"Cryospheric studies and observations","score":0.9760000109672546,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9700000286102295,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6438875198364258},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5326375961303711},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.457756370306015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2870773673057556},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24148797988891602}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6438875198364258},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5326375961303711},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.457756370306015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2870773673057556},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24148797988891602}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss53475.2024.10642139","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/igarss53475.2024.10642139","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.800000011920929,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1781084009","https://openalex.org/W2071341963","https://openalex.org/W2097317468","https://openalex.org/W2129465030","https://openalex.org/W2134159265","https://openalex.org/W2145016426","https://openalex.org/W2166964243","https://openalex.org/W2801080873","https://openalex.org/W2908740848","https://openalex.org/W2964015378","https://openalex.org/W2985331920","https://openalex.org/W3173138024","https://openalex.org/W3205572016","https://openalex.org/W4200515797","https://openalex.org/W4206815052","https://openalex.org/W4234337445","https://openalex.org/W4255440115","https://openalex.org/W4283750495","https://openalex.org/W4386018510","https://openalex.org/W4387058862","https://openalex.org/W6726873649","https://openalex.org/W6892121078","https://openalex.org/W6929875479"],"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/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"The":[0,70,88,122],"Ice-sheet":[1],"and":[2,21,74,93,146],"Sea-level":[3],"System":[4],"Model":[5],"(ISSM)":[6],"provides":[7],"solutions":[8],"for":[9,68],"Stokes":[10],"equations":[11],"relevant":[12],"to":[13,128],"ice":[14,91],"sheet":[15],"dynamics":[16],"by":[17,49],"employing":[18],"finite":[19,28],"element":[20,29],"fine":[22],"mesh":[23],"adaption.":[24],"However,":[25],"since":[26],"its":[27],"method":[30],"is":[31,72],"compatible":[32],"only":[33],"with":[34,95,143],"Central":[35],"Processing":[36,54],"Units":[37,55],"(CPU),":[38],"the":[39,77,83,103,118,131,136],"ISSM":[40,80,120],"has":[41],"limits":[42],"on":[43],"further":[44],"economizing":[45],"computational":[46,115,149],"time.":[47,150],"Thus,":[48],"taking":[50],"advantage":[51],"of":[52],"Graphics":[53],"(GPUs),":[56],"we":[57],"design":[58],"a":[59,65,96],"graph":[60],"convolutional":[61,105],"network":[62,107],"(GCN)":[63],"as":[64],"fast":[66],"emulator":[67,125],"ISSM.":[69],"GCN":[71,89,110,124],"trained":[73],"tested":[75],"using":[76],"20-year":[78],"transient":[79],"simulations":[81],"in":[82,135],"Pine":[84],"Island":[85],"Glacier":[86],"(PIG).":[87],"reproduces":[90],"thickness":[92],"velocity":[94],"correlation":[97],"coefficient":[98],"greater":[99],"than":[100,117],"0.998,":[101],"outperforming":[102],"traditional":[104],"neural":[106],"(CNN).":[108],"Additionally,":[109],"shows":[111],"34":[112],"times":[113],"faster":[114,148],"speed":[116],"CPU-based":[119],"modeling.":[121],"GPU-based":[123],"allows":[126],"us":[127],"predict":[129],"how":[130],"PIG":[132],"will":[133],"change":[134],"future":[137],"under":[138],"different":[139],"melting":[140],"rate":[141],"scenarios":[142],"high":[144],"fidelity":[145],"much":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
