{"id":"https://openalex.org/W7108751399","doi":"https://doi.org/10.1016/j.engappai.2025.113398","title":"Learning from more to predict with less: Representation-level multimodal distillation to address training\u2013inference data asymmetry in surrogate modeling","display_name":"Learning from more to predict with less: Representation-level multimodal distillation to address training\u2013inference data asymmetry in surrogate modeling","publication_year":2025,"publication_date":"2025-12-05","ids":{"openalex":"https://openalex.org/W7108751399","doi":"https://doi.org/10.1016/j.engappai.2025.113398"},"language":"en","primary_location":{"id":"doi:10.1016/j.engappai.2025.113398","is_oa":false,"landing_page_url":"https://doi.org/10.1016/j.engappai.2025.113398","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-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":null,"display_name":"Shayan Mazloom","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shayan Mazloom","raw_affiliation_strings":["Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA"],"affiliations":[{"raw_affiliation_string":"Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Abdollah Shafieezadeh","orcid":"https://orcid.org/0000-0001-6768-8522"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Abdollah Shafieezadeh","raw_affiliation_strings":["Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA"],"affiliations":[{"raw_affiliation_string":"Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jieun Hur","orcid":"https://orcid.org/0000-0002-2997-5544"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jieun Hur","raw_affiliation_strings":["Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA"],"affiliations":[{"raw_affiliation_string":"Risk Assessment and Management of Structural and Infrastructural Systems (RAMSIS) Laboratory, Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, 43210, OH, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jae-Wook Jung","orcid":null},"institutions":[{"id":"https://openalex.org/I155671955","display_name":"Korea Atomic Energy Research Institute","ror":"https://ror.org/01xb4fs50","country_code":"KR","type":"facility","lineage":["https://openalex.org/I155671955","https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae-Wook Jung","raw_affiliation_strings":["Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea","institution_ids":["https://openalex.org/I155671955"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jeong-Gon Ha","orcid":"https://orcid.org/0000-0001-8932-4738"},"institutions":[{"id":"https://openalex.org/I155671955","display_name":"Korea Atomic Energy Research Institute","ror":"https://ror.org/01xb4fs50","country_code":"KR","type":"facility","lineage":["https://openalex.org/I155671955","https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jeong-Gon Ha","raw_affiliation_strings":["Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea","institution_ids":["https://openalex.org/I155671955"]}]},{"author_position":"last","author":{"id":null,"display_name":"Daegi Hahm","orcid":null},"institutions":[{"id":"https://openalex.org/I155671955","display_name":"Korea Atomic Energy Research Institute","ror":"https://ror.org/01xb4fs50","country_code":"KR","type":"facility","lineage":["https://openalex.org/I155671955","https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Daegi Hahm","raw_affiliation_strings":["Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Advanced Structures and Seismic Safety Research Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Republic of Korea","institution_ids":["https://openalex.org/I155671955"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":{"value":3170,"currency":"USD","value_usd":3170},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6091623,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"165","issue":null,"first_page":"113398","last_page":"113398"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.8884999752044678,"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":0.8884999752044678,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05000000074505806,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.009600000455975533,"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/inference","display_name":"Inference","score":0.5906999707221985},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.47609999775886536},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44110000133514404},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4293000102043152},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.3765000104904175},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.3758000135421753},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.3747999966144562},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.37070000171661377},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.3522000014781952},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.3375000059604645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8507999777793884},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6385999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6047999858856201},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5906999707221985},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.47609999775886536},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44110000133514404},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4293000102043152},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2903999984264374},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C124978682","wikidata":"https://www.wikidata.org/wiki/Q1201019","display_name":"Proof of concept","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2628999948501587},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.engappai.2025.113398","is_oa":false,"landing_page_url":"https://doi.org/10.1016/j.engappai.2025.113398","pdf_url":null,"source":{"id":"https://openalex.org/S900972176","display_name":"Engineering Applications of Artificial Intelligence","issn_l":"0952-1976","issn":["0952-1976","1873-6769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Applications of Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1296284870","display_name":null,"funder_award_id":"2000156","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G957854714","display_name":null,"funder_award_id":"20224B10200040","funder_id":"https://openalex.org/F4320321681","funder_display_name":"Ministry of Trade, Industry and Energy"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309374","display_name":"Ohio State University","ror":"https://ror.org/00rs6vg23"},{"id":"https://openalex.org/F4320321681","display_name":"Ministry of Trade, Industry and Energy","ror":"https://ror.org/008nkqk13"},{"id":"https://openalex.org/F4320335199","display_name":"Korea Institute of Energy Technology Evaluation and Planning","ror":"https://ror.org/02zq38y32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2010509626","https://openalex.org/W2108880624","https://openalex.org/W2141510836","https://openalex.org/W2159934755","https://openalex.org/W2165008991","https://openalex.org/W2619383789","https://openalex.org/W2900833721","https://openalex.org/W2949676527","https://openalex.org/W3015706362","https://openalex.org/W3040190702","https://openalex.org/W4205521769","https://openalex.org/W4292397407","https://openalex.org/W4296309188","https://openalex.org/W4306726700","https://openalex.org/W4383336751","https://openalex.org/W4391288546","https://openalex.org/W4393181120","https://openalex.org/W4396927180","https://openalex.org/W4399805677","https://openalex.org/W4401047100","https://openalex.org/W4403998447","https://openalex.org/W4411308264","https://openalex.org/W4411508044","https://openalex.org/W4412552357"],"related_works":[],"abstract_inverted_index":null,"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-12-05T00:00:00"}