{"id":"https://openalex.org/W6912017051","doi":"https://doi.org/10.5281/zenodo.15687518","title":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning (Dataset)","display_name":"Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning (Dataset)","publication_year":2025,"publication_date":"2025-06-18","ids":{"openalex":"https://openalex.org/W6912017051","doi":"https://doi.org/10.5281/zenodo.15687518"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.15687518","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15687518","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.15687518","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Berner, Julius","orcid":"https://orcid.org/0000-0002-5648-648X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Berner, Julius","raw_affiliation_strings":["NVIDIA"],"raw_orcid":"https://orcid.org/0000-0002-5648-648X","affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Liu-Schiaffini, Miguel","orcid":"https://orcid.org/0000-0001-9685-8383"},"institutions":[{"id":"https://openalex.org/I122411786","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055","country_code":"US","type":"education","lineage":["https://openalex.org/I122411786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liu-Schiaffini, Miguel","raw_affiliation_strings":["California Institute of Technology"],"raw_orcid":"https://orcid.org/0000-0001-9685-8383","affiliations":[{"raw_affiliation_string":"California Institute of Technology","institution_ids":["https://openalex.org/I122411786"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Kossaifi, Jean","orcid":"https://orcid.org/0000-0002-4445-3429"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kossaifi, Jean","raw_affiliation_strings":["NVIDIA"],"raw_orcid":"https://orcid.org/0000-0002-4445-3429","affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Duruisseaux, Valentin","orcid":"https://orcid.org/0000-0002-8550-3058"},"institutions":[{"id":"https://openalex.org/I122411786","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055","country_code":"US","type":"education","lineage":["https://openalex.org/I122411786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duruisseaux, Valentin","raw_affiliation_strings":["California Institute of Technology"],"raw_orcid":"https://orcid.org/0000-0002-8550-3058","affiliations":[{"raw_affiliation_string":"California Institute of Technology","institution_ids":["https://openalex.org/I122411786"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bonev, Boris","orcid":"https://orcid.org/0000-0002-4041-238X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bonev, Boris","raw_affiliation_strings":["NVIDIA"],"raw_orcid":"https://orcid.org/0000-0002-4041-238X","affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Azizzadenesheli, Kamyar","orcid":"https://orcid.org/0000-0001-8507-1868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Azizzadenesheli, Kamyar","raw_affiliation_strings":["NVIDIA"],"raw_orcid":"https://orcid.org/0000-0001-8507-1868","affiliations":[{"raw_affiliation_string":"NVIDIA","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Anandkumar, Anima","orcid":"https://orcid.org/0000-0002-6974-6797"},"institutions":[{"id":"https://openalex.org/I122411786","display_name":"California Institute of Technology","ror":"https://ror.org/05dxps055","country_code":"US","type":"education","lineage":["https://openalex.org/I122411786"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anandkumar, Anima","raw_affiliation_strings":["California Institute of Technology"],"raw_orcid":"https://orcid.org/0000-0002-6974-6797","affiliations":[{"raw_affiliation_string":"California Institute of Technology","institution_ids":["https://openalex.org/I122411786"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6363000273704529},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5971999764442444},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5763999819755554},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5360999703407288},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5116999745368958},{"id":"https://openalex.org/keywords/implementation","display_name":"Implementation","score":0.48649999499320984},{"id":"https://openalex.org/keywords/bitwise-operation","display_name":"Bitwise operation","score":0.47600001096725464},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.44839999079704285}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.770799994468689},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6363000273704529},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5763999819755554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5763000249862671},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5360999703407288},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5116999745368958},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.48649999499320984},{"id":"https://openalex.org/C134765980","wikidata":"https://www.wikidata.org/wiki/Q879126","display_name":"Bitwise operation","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.44839999079704285},{"id":"https://openalex.org/C2778671685","wikidata":"https://www.wikidata.org/wiki/Q219239","display_name":"Recipe","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27889999747276306},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2727000117301941}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.15687518","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15687518","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.15687518","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15687518","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4971636235713959}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"the":[2,5,26,78,101,105],"datasets":[3,107],"for":[4,9,16,29,46,89],"paper":[6,59],"\"Principled":[7],"Approaches":[8],"Extending":[10],"Neural":[11],"Architectures":[12],"to":[13],"Function":[14],"Spaces":[15],"Operator":[17],"Learning.\"":[18],"In":[19],"this":[20],"paper,":[21],"we":[22,42,83],"identify":[23],"and":[24,73,87,99],"distill":[25],"key":[27],"principles":[28],"constructing":[30],"practical":[31],"implementations":[32],"of":[33],"mappings":[34],"between":[35],"infinite-dimensional":[36],"function":[37],"spaces.":[38],"Using":[39],"these":[40],"principles,":[41],"propose":[43],"a":[44,63],"recipe":[45],"converting":[47],"several":[48,67],"popular":[49],"neural":[50,53],"architectures":[51],"into":[52],"operators":[54],"with":[55],"minimal":[56],"modifications.":[57],"Our":[58],"includes":[60],"experiments":[61],"on":[62],"Navier-Stokes":[64],"dataset":[65],"at":[66],"resolutions:":[68],"64x64,":[69],"128x128,":[70],"256x256,":[71],"512x512":[72],"1024x1024.":[74],"This":[75],"repository":[76,97],"contains":[77],"exact":[79],"training/testing":[80,102],"split":[81],"that":[82],"used":[84],"in":[85,94],"training":[86],"evaluation":[88],"our":[90,95],"experiments.":[91],"The":[92],"code":[93],"GitHub":[96],"preprocesses":[98],"constructs":[100],"data":[103],"from":[104],"raw":[106],"above.":[108]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}