{"id":"https://openalex.org/W7136135423","doi":"https://doi.org/10.48550/arxiv.2603.13227","title":"Representation Learning for Spatiotemporal Physical Systems","display_name":"Representation Learning for Spatiotemporal Physical Systems","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7136135423","doi":"https://doi.org/10.48550/arxiv.2603.13227"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13227","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13227","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122475336","display_name":"Helen Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qu, Helen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124910145","display_name":"Rudy Morel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morel, Rudy","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129481913","display_name":"Michael McCabe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"McCabe, Michael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088570356","display_name":"Alberto Bietti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bietti, Alberto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021502333","display_name":"Fran\u00e7ois Lanusse","orcid":"https://orcid.org/0000-0001-7956-0542"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lanusse, Fran\u00e7ois","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129523247","display_name":"Shirley Ho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ho, Shirley","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129605843","display_name":"Yann LeCun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LeCun, Yann","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5122475336"],"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":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.17800000309944153,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.17800000309944153,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.1648000031709671,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14100000262260437,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/embedding","display_name":"Embedding","score":0.5511999726295471},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5120999813079834},{"id":"https://openalex.org/keywords/physical-system","display_name":"Physical system","score":0.5049999952316284},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5029000043869019},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4659000039100647},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4316999912261963},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.383899986743927},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.3626999855041504}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7513999938964844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6582000255584717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6389999985694885},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5120999813079834},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5029000043869019},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4316999912261963},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.383899986743927},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.3626999855041504},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.31299999356269836},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.29750001430511475},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.2590999901294708},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2522999942302704},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13227","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13227","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13227","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1,17,105,129],"approaches":[2],"to":[3,33,38],"spatiotemporal":[4],"physical":[5,75,88,124],"systems":[6],"have":[7],"primarily":[8],"focused":[9],"on":[10,78,131],"next-frame":[11],"prediction,":[12],"with":[13],"the":[14,22,65,87,91,98,139],"goal":[15],"of":[16,63,71,90,93,100],"an":[18],"accurate":[19],"emulator":[20],"for":[21,111,123],"system's":[23,73],"evolution":[24],"in":[25,104,138],"time.":[26],"However,":[27],"these":[28,79,94,132],"emulators":[29],"are":[30,36,109],"computationally":[31],"expensive":[32],"train":[34],"and":[35,56,134],"subject":[37],"performance":[39],"pitfalls,":[40],"such":[41,68],"as":[42,69],"compounding":[43],"errors":[44],"during":[45],"autoregressive":[46],"rollout.":[47],"In":[48],"this":[49],"work,":[50],"we":[51,116],"take":[52],"a":[53,72,82],"different":[54],"perspective":[55],"look":[57],"at":[58,158],"scientific":[59,113],"tasks":[60,80],"further":[61],"downstream":[62,112],"predicting":[64],"next":[66],"frame,":[67],"estimation":[70],"governing":[74],"parameters.":[76],"Accuracy":[77],"offers":[81],"uniquely":[83],"quantifiable":[84],"glimpse":[85],"into":[86],"relevance":[89],"representations":[92,107],"models.":[95],"We":[96],"evaluate":[97],"effectiveness":[99],"general-purpose":[101],"self-supervised":[102,128],"methods":[103,121,130,135],"physics-grounded":[106],"that":[108,118,136],"useful":[110],"tasks.":[114],"Surprisingly,":[115],"find":[117],"not":[119],"all":[120],"designed":[122],"modeling":[125],"outperform":[126,149],"generic":[127],"tasks,":[133],"learn":[137],"latent":[140],"space":[141],"(e.g.,":[142],"joint":[143],"embedding":[144],"predictive":[145],"architectures,":[146],"or":[147],"JEPAs)":[148],"those":[150],"optimizing":[151],"pixel-level":[152],"prediction":[153],"objectives.":[154],"Code":[155],"is":[156],"available":[157],"https://github.com/helenqu/physical-representation-learning.":[159]},"counts_by_year":[],"updated_date":"2026-03-17T07:05:13.627479","created_date":"2026-03-17T00:00:00"}
