{"id":"https://openalex.org/W7225420086","doi":"https://doi.org/10.21203/rs.3.rs-11611326/v1","title":"Physical fidelity and forecasting in compressed active matter representations","display_name":"Physical fidelity and forecasting in compressed active matter representations","publication_year":2026,"publication_date":"2026-11-06","ids":{"openalex":"https://openalex.org/W7225420086","doi":"https://doi.org/10.21203/rs.3.rs-11611326/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-11611326/v1","doi":"https://doi.org/10.21203/rs.3.rs-11611326/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11611326/v1","pdf_url":"https://www.researchsquare.com/article/rs-11611326/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.researchsquare.com/article/rs-11611326/latest.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161103939","display_name":"Leonel Guaman","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3130401523","display_name":"Universidad Yachay Tech","ror":"https://ror.org/04jjswc10","country_code":"EC","type":"education","lineage":["https://openalex.org/I3130401523"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Leonel Guaman","raw_affiliation_strings":["Yachay Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yachay Tech","institution_ids":["https://openalex.org/I3130401523"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161046325","display_name":"Franklin Limaico","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3130401523","display_name":"Universidad Yachay Tech","ror":"https://ror.org/04jjswc10","country_code":"EC","type":"education","lineage":["https://openalex.org/I3130401523"]}],"countries":["EC"],"is_corresponding":false,"raw_author_name":"Franklin Limaico","raw_affiliation_strings":["Yachay Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yachay Tech","institution_ids":["https://openalex.org/I3130401523"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043253041","display_name":"Hern\u00e1n Andr\u00e9s Morales-Navarrete","orcid":"https://orcid.org/0000-0002-9578-2556","observed_orcids":["https://orcid.org/0000-0002-9578-2556"]},"institutions":[{"id":"https://openalex.org/I4210100770","display_name":"University of the Americas","ror":"https://ror.org/0166e9x11","country_code":"CL","type":"education","lineage":["https://openalex.org/I4210100770"]},{"id":"https://openalex.org/I4210102282","display_name":"Universidad de Las Am\u00e9ricas","ror":"https://ror.org/0198j4566","country_code":"EC","type":"education","lineage":["https://openalex.org/I4210102282"]},{"id":"https://openalex.org/I4210120382","display_name":"Universidad de las Am\u00e9ricas","ror":"https://ror.org/02j003371","country_code":"MX","type":"education","lineage":["https://openalex.org/I4210120382"]}],"countries":["CL","EC","MX"],"is_corresponding":false,"raw_author_name":"Hern\u00e1n Morales-Navarrete","raw_affiliation_strings":["Universidad de Las Am\u00e9ricas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Las Am\u00e9ricas","institution_ids":["https://openalex.org/I4210100770","https://openalex.org/I4210102282","https://openalex.org/I4210120382"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"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/T11486","display_name":"Micro and Nano Robotics","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter 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"}}],"keywords":[{"id":"https://openalex.org/keywords/active-matter","display_name":"active matter","score":0.8510000109672546},{"id":"https://openalex.org/keywords/active-suspension","display_name":"active suspension","score":0.8759999871253967},{"id":"https://openalex.org/keywords/compressed-representations","display_name":"compressed representations","score":0.7139999866485596},{"id":"https://openalex.org/keywords/physical-constraints","display_name":"physical constraints","score":0.7239999771118164},{"id":"https://openalex.org/keywords/autoencoder","display_name":"autoencoder","score":0.8830000162124634},{"id":"https://openalex.org/keywords/nematic-order","display_name":"nematic order","score":0.6449999809265137},{"id":"https://openalex.org/keywords/enstrophy","display_name":"enstrophy","score":0.8600000143051147},{"id":"https://openalex.org/keywords/forecasting","display_name":"forecasting","score":0.7269999980926514},{"id":"https://openalex.org/keywords/model-reduction","display_name":"model reduction","score":0.5249999761581421},{"id":"https://openalex.org/keywords/physical-admissibility","display_name":"physical admissibility","score":0.6159999966621399}],"concepts":[{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.8626000285148621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5511999726295471},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.477400004863739},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41760000586509705},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3912999927997589},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3382999897003174},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27390000224113464},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-11611326/v1","doi":"https://doi.org/10.21203/rs.3.rs-11611326/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11611326/v1","pdf_url":"https://www.researchsquare.com/article/rs-11611326/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-11611326/v1","doi":"https://doi.org/10.21203/rs.3.rs-11611326/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11611326/v1","pdf_url":"https://www.researchsquare.com/article/rs-11611326/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225420086.pdf","grobid_xml":"https://content.openalex.org/works/W7225420086.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W2029189987","https://openalex.org/W2074574519","https://openalex.org/W4211209035","https://openalex.org/W4281383877","https://openalex.org/W4327695944","https://openalex.org/W4392104288","https://openalex.org/W4400182292","https://openalex.org/W4400190176","https://openalex.org/W4415797834","https://openalex.org/W7121693456","https://openalex.org/W7136135423"],"related_works":["https://openalex.org/W1610276767","https://openalex.org/W7223390047","https://openalex.org/W4365461210","https://openalex.org/W7225296941","https://openalex.org/W7160381497","https://openalex.org/W4385544362","https://openalex.org/W2886997576","https://openalex.org/W7101771734","https://openalex.org/W3183161202","https://openalex.org/W7165132605"],"abstract_inverted_index":{"Abstract":[0],"Reduced":[1],"descriptions":[2],"of":[3,14,38,92,112],"active":[4,41],"fluids":[5],"must":[6],"capture":[7],"physically":[8],"meaningful":[9],"states":[10],"and":[11,33,59,83,94,117,156,198,208],"support":[12],"prediction":[13],"their":[15,190],"evolution,":[16],"yet":[17],"the":[18,69,72,113,134,145,163,195],"relationship":[19],"between":[20],"these":[21,178],"requirements":[22,213],"remains":[23],"unclear.":[24],"Here,":[25],"we":[26],"investigate":[27],"how":[28],"physical":[29,182,204],"constraints":[30,70,183],"affect":[31],"reconstruction":[32,75,175],"forecasting":[34],"in":[35,101,121],"compressed":[36],"representations":[37],"a":[39,98],"simulated":[40],"suspension.":[42],"We":[43],"compare":[44],"autoencoders":[45,58],"incorporating":[46],"soft":[47],"cross-field":[48],"penalties,":[49],"hard":[50],"concentration\u2013orientation":[51],"moment":[52],"realizability,":[53],"or":[54],"both,":[55],"with":[56,96],"unconstrained":[57,146],"weighted":[60],"proper":[61],"orthogonal":[62],"decomposition":[63],"at":[64,89,162],"matched":[65],"latent":[66,90,132,165],"dimensions.":[67],"Combining":[68],"reduces":[71,137],"aggregate":[73],"standardized":[74],"error":[76,140],"across":[77],"spatially":[78],"averaged":[79],"velocity":[80],"intensity,":[81],"enstrophy":[82],"local":[84],"nematic":[85],"order":[86],"by":[87,141],"51\u201357%":[88],"dimensions":[91],"128":[93],"256,":[95],"approximately":[97],"1%":[99],"increase":[100],"balanced":[102],"field":[103,151,155],"error.":[104,152],"The":[105],"combined":[106,135],"model":[107,136,197],"also":[108],"eliminates":[109],"detected":[110],"violations":[111],"enforced":[114],"admissibility":[115],"conditions":[116],"outperforms":[118],"post-inference":[119],"projection":[120],"observable":[122,139,157,171,187],"reconstruction.":[123],"These":[124],"benefits":[125,192],"extend":[126],"selectively":[127],"to":[128,144],"forecasting.":[129],"With":[130],"linear":[131],"dynamics,":[133],"ten-step":[138],"10\u201313%":[142],"relative":[143],"autoencoder,":[147],"while":[148,189],"slightly":[149],"increasing":[150],"Against":[153],"persistence,":[154],"metrics":[158],"yield":[159],"opposite":[160],"rankings":[161],"reference":[164],"dimension.":[166],"Linear-readout":[167],"analyses":[168],"further":[169],"distinguish":[170],"accessibility":[172],"from":[173],"decoder":[174],"accuracy.":[176],"Together,":[177],"results":[179],"demonstrate":[180],"that":[181],"can":[184],"substantially":[185],"improve":[186],"preservation,":[188],"predictive":[191],"depend":[193],"on":[194],"temporal":[196,209],"quantity":[199],"evaluated.":[200],"They":[201],"motivate":[202],"assessing":[203],"admissibility,":[205],"retained":[206],"information":[207],"accuracy":[210],"as":[211],"complementary":[212],"for":[214],"reduced":[215],"active-fluid":[216],"models.":[217]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-07T00:00:00"}