{"id":"https://openalex.org/W7204162538","doi":"https://doi.org/10.48550/arxiv.2608.22277","title":"Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems","display_name":"Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems","publication_year":2026,"publication_date":"2026-08-23","ids":{"openalex":"https://openalex.org/W7204162538","doi":"https://doi.org/10.48550/arxiv.2608.22277"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.22277","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22277","pdf_url":"https://arxiv.org/pdf/2608.22277","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":"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/2608.22277","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fang, Zhou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Fang, Zhou","raw_affiliation_strings":["Department of Mechanical Engineering National University of Singapore Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering National University of Singapore Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091468612","display_name":"Gianmarco Mengaldo","orcid":"https://orcid.org/0000-0002-0157-5477","observed_orcids":["https://orcid.org/0000-0002-0157-5477"]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Mengaldo, Gianmarco","raw_affiliation_strings":["Department of Mathematics National University of Singapore Singapore","Department of Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics National University of Singapore Singapore","institution_ids":["https://openalex.org/I165932596"]},{"raw_affiliation_string":"Department of Mechanical Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"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":0.9955999851226807,"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":0.9955999851226807,"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":0.001500000013038516,"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/T10244","display_name":"Chaos control and synchronization","score":0.00139999995008111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-reweighting","display_name":"sample reweighting","score":0.7609999775886536},{"id":"https://openalex.org/keywords/chaotic-systems","display_name":"chaotic systems","score":0.6639999747276306},{"id":"https://openalex.org/keywords/deep-learning-prediction","display_name":"deep learning prediction","score":0.5519999861717224},{"id":"https://openalex.org/keywords/local-dimension","display_name":"local dimension","score":0.7829999923706055},{"id":"https://openalex.org/keywords/imbalanced-regression","display_name":"imbalanced regression","score":0.6729999780654907},{"id":"https://openalex.org/keywords/recurrent-states","display_name":"recurrent states","score":0.35499998927116394},{"id":"https://openalex.org/keywords/loss-landscape","display_name":"loss landscape","score":0.36000001430511475}],"concepts":[{"id":"https://openalex.org/C164380108","wikidata":"https://www.wikidata.org/wiki/Q507187","display_name":"Attractor","level":2,"score":0.7429999709129333},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.6902999877929688},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6607999801635742},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6251999735832214},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.597100019454956},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5548999905586243},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5281999707221985},{"id":"https://openalex.org/C151342819","wikidata":"https://www.wikidata.org/wiki/Q62542","display_name":"Phase space","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C33962884","wikidata":"https://www.wikidata.org/wiki/Q378637","display_name":"Dynamical system (definition)","level":3,"score":0.39070001244544983},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3634999990463257},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C43456602","wikidata":"https://www.wikidata.org/wiki/Q7303254","display_name":"Recurrence quantification analysis","level":3,"score":0.35409998893737793},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3474000096321106},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3411000072956085},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3310000002384186},{"id":"https://openalex.org/C39630112","wikidata":"https://www.wikidata.org/wiki/Q5178018","display_name":"Coupled map lattice","level":5,"score":0.3190000057220459},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.29589998722076416},{"id":"https://openalex.org/C92866567","wikidata":"https://www.wikidata.org/wiki/Q166314","display_name":"Chaos theory","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C197247276","wikidata":"https://www.wikidata.org/wiki/Q5072843","display_name":"Chaotic scattering","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C2781402108","wikidata":"https://www.wikidata.org/wiki/Q1642961","display_name":"H\u00e9non map","level":3,"score":0.25760000944137573},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.2572999894618988},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.2563000023365021}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.22277","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22277","pdf_url":"https://arxiv.org/pdf/2608.22277","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.22277","doi":"https://doi.org/10.48550/arxiv.2608.22277","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.22277","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:2608.22277","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22277","pdf_url":"https://arxiv.org/pdf/2608.22277","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204162538.pdf","grobid_xml":"https://content.openalex.org/works/W7204162538.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2177410802","https://openalex.org/W3118310857","https://openalex.org/W4221146190","https://openalex.org/W7083443619","https://openalex.org/W7124420056","https://openalex.org/W7138414029"],"related_works":["https://openalex.org/W4318812276","https://openalex.org/W7161299488","https://openalex.org/W4411644343","https://openalex.org/W4416292402","https://openalex.org/W7212385509","https://openalex.org/W6974260812","https://openalex.org/W4367060971","https://openalex.org/W2953065922","https://openalex.org/W7162991370","https://openalex.org/W6910627424"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"surrogates":[2,71],"have":[3],"become":[4],"powerful":[5],"tools":[6],"for":[7],"simulating":[8],"and":[9,54,58,197],"forecasting":[10],"complex":[11,60,235],"dynamical":[12,117,146,157],"systems,":[13],"yet":[14],"their":[15,73],"utility":[16],"remains":[17],"limited":[18],"by":[19,49,56,100,217],"catastrophic":[20],"error":[21,205,221],"accumulation":[22],"during":[23,224],"long-term":[24,203],"autoregressive":[25,204],"rollouts.":[26],"This":[27],"behavior":[28],"is":[29],"partly":[30],"tied":[31],"to":[32,76,104,164,207],"the":[33,36,77,86,115,121,125,141,161,169,180,219,228,242],"nature":[34],"of":[35,124,154],"underlying":[37],"systems:":[38],"chaotic":[39,181],"spatiotemporal":[40],"systems":[41,147],"visit":[42],"phase":[43],"space":[44],"unevenly,":[45],"with":[46,114],"dynamics":[47],"dominated":[48],"recurrent,":[50],"low-dimensional":[51,81],"quiescent":[52,82],"states":[53],"characterized":[55],"rare":[57],"dynamically":[59],"regime":[61],"transitions.":[62],"Trained":[63],"under":[64],"a":[65,135,151,155],"sample-wise":[66],"uniform":[67,187],"objective,":[68],"standard":[69],"neural":[70],"allocate":[72,165],"finite":[74],"capacity":[75,167],"statistically":[78],"more":[79],"numerous":[80],"states,":[83],"systematically":[84,177],"under-representing":[85],"transient":[87],"regimes":[88,172],"that":[89,213],"trigger":[90],"disproportionate,":[91],"localized":[92,220],"errors.":[93],"Existing":[94],"imbalanced-regression":[95],"methods":[96],"tackle":[97],"this":[98,216],"issue":[99],"reweighting":[101,138],"samples":[102],"according":[103],"target-space":[105,109,195],"density.":[106],"However,":[107],"statistical":[108],"rarity":[110,118],"does":[111],"not":[112],"coincide":[113],"intrinsic":[116],"encoded":[119],"in":[120,227,241],"recurrence":[122],"geometry":[123],"attractor.":[126],"To":[127],"address":[128],"this,":[129],"we":[130],"introduce":[131],"Dynamics-Aware":[132],"Weighting":[133],"(DAW),":[134],"data-centric":[136],"objective":[137],"framework.":[139],"Using":[140],"local":[142,229],"dimension":[143,230],"$d$":[144],"from":[145],"theory":[148],"as":[149,189,191,239],"an":[150],"priori":[152],"measure":[153],"state's":[156],"complexity,":[158],"DAW":[159,184,214],"reshapes":[160],"loss":[162],"landscape":[163],"representational":[166],"toward":[168],"sparse,":[170],"high-$d$":[171],"where":[173],"forecast":[174],"errors":[175],"are":[176],"large.":[178],"On":[179],"KS":[182,243],"equation,":[183],"consistently":[185],"outperforms":[186],"training":[188],"well":[190],"weighting":[192],"based":[193],"on":[194],"rarity,":[196],"its":[198],"randomly":[199],"permuted":[200],"ablation,":[201],"reducing":[202],"relative":[206],"all":[208],"baselines.":[209],"Event-level":[210],"analysis":[211],"shows":[212],"achieves":[215],"suppressing":[218],"amplifications":[222],"incurred":[223],"sharp":[225],"jumps":[226],"$d$,":[231],"which":[232],"typically":[233],"accompany":[234],"physical":[236],"processes":[237],"such":[238],"wave-merging":[240],"system.":[244]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-26T00:00:00"}