{"id":"https://openalex.org/W7214289231","doi":"https://doi.org/10.48550/arxiv.2609.29403","title":"RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations","display_name":"RD-JEPA: Predictive latent pretraining for few-trajectory transfer across reaction--diffusion equations","publication_year":2026,"publication_date":"2026-09-24","ids":{"openalex":"https://openalex.org/W7214289231","doi":"https://doi.org/10.48550/arxiv.2609.29403"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.29403","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.29403","pdf_url":"https://arxiv.org/pdf/2609.29403","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.29403","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055897166","display_name":"Chenhao Si","orcid":"https://orcid.org/0009-0006-5314-4632","observed_orcids":["https://orcid.org/0009-0006-5314-4632"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Si, Chenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5152897521","display_name":"Ming Yan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Yan, Ming","raw_affiliation_strings":["School of Data Science , The Chinese University of Hong Kong , Shenzhen , Shenzhen , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Data Science , The Chinese University of Hong Kong , Shenzhen , Shenzhen , China","institution_ids":["https://openalex.org/I177725633","https://openalex.org/I4210116924"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11206","display_name":"Model Reduction and Neural Networks","score":1.0,"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":1.0,"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/T10320","display_name":"Neural Networks and Applications","score":0.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0,"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/reaction-diffusion-systems","display_name":"reaction-diffusion systems","score":0.9639999866485596},{"id":"https://openalex.org/keywords/self-supervised-pretraining","display_name":"self-supervised pre-training","score":0.7210000157356262},{"id":"https://openalex.org/keywords/time-dependent-partial-differential-equations","display_name":"time-dependent partial differential equations","score":0.6520000100135803},{"id":"https://openalex.org/keywords/joint-embedding","display_name":"joint embedding","score":0.414000004529953},{"id":"https://openalex.org/keywords/physics-informed-machine-learning","display_name":"physics-informed machine learning","score":0.5099999904632568}],"concepts":[{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5058000087738037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47600001096725464},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44110000133514404},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4323999881744385},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41589999198913574},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.34709998965263367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34150001406669617},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26330000162124634},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.257999986410141}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.29403","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.29403","pdf_url":"https://arxiv.org/pdf/2609.29403","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.29403","doi":"https://doi.org/10.48550/arxiv.2609.29403","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.29403","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.29403","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.29403","pdf_url":"https://arxiv.org/pdf/2609.29403","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1658892879","display_name":null,"funder_award_id":"72495131","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6494783278","display_name":null,"funder_award_id":"82441027","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214289231.pdf","grobid_xml":"https://content.openalex.org/works/W7214289231.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W7136135423","https://openalex.org/W7148932850"],"related_works":["https://openalex.org/W4407948985","https://openalex.org/W7148932850","https://openalex.org/W7214733027","https://openalex.org/W4318347260","https://openalex.org/W4409486766","https://openalex.org/W7128640606","https://openalex.org/W7223122371","https://openalex.org/W2981783904","https://openalex.org/W4410592681","https://openalex.org/W4385373641"],"abstract_inverted_index":{"Learning":[0],"surrogates":[1],"for":[2,25],"time-dependent":[3],"partial":[4],"differential":[5],"equations":[6],"often":[7],"requires":[8],"a":[9,21,64],"new":[10],"simulation":[11],"corpus":[12],"when":[13],"the":[14,93,106,118],"governing":[15],"operator":[16],"changes.":[17],"We":[18],"introduce":[19],"RD-JEPA,":[20],"joint-embedding":[22],"predictive":[23,95],"architecture":[24],"self-supervised":[26],"pretraining":[27],"on":[28,36],"reaction-diffusion":[29,132],"trajectories.":[30],"A":[31],"single":[32],"model":[33,101],"is":[34],"pretrained":[35],"five":[37,83],"parameterized":[38],"systems":[39,46],"and":[40,50,76,98,113],"then":[41],"adapted":[42],"to":[43],"three":[44],"held-out":[45,65],"whose":[47],"reaction":[48],"operators":[49],"trajectories":[51,62],"are":[52],"excluded":[53],"from":[54,63,103],"pretraining.":[55],"Using":[56],"one,":[57],"five,":[58],"or":[59],"ten":[60],"complete":[61],"system,":[66],"RD-JEPA":[67],"achieves":[68],"lower":[69],"mean":[70,77],"relative":[71],"discrete":[72],"$\\ell^2$":[73],"field":[74],"error":[75,81],"absolute":[78],"spatial":[79],"first-difference":[80],"than":[82],"supervised":[84],"surrogate":[85],"baselines,":[86],"an":[87,99],"independently":[88],"trained":[89,102],"control":[90],"that":[91,121],"removes":[92],"trajectory-dependent":[94],"latent":[96],"pathway,":[97],"architecture-matched":[100],"scratch.":[104],"Within":[105],"evaluated":[107],"equations,":[108],"output":[109],"resolution,":[110],"forecast":[111],"horizons,":[112],"choices":[114],"of":[115,123],"adaptation":[116,129],"trajectories,":[117],"results":[119],"indicate":[120],"prediction":[122],"future-state":[124],"representations":[125],"can":[126],"support":[127],"data-efficient":[128],"across":[130],"related":[131],"systems.":[133]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-09-26T00:00:00"}