{"id":"https://openalex.org/W7162514838","doi":"https://doi.org/10.48550/arxiv.2605.26732","title":"APEX: Amplitude Anchors and Phase Priors for Target-Scarce Higher-Frequency Wave Prediction","display_name":"APEX: Amplitude Anchors and Phase Priors for Target-Scarce Higher-Frequency Wave Prediction","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162514838","doi":"https://doi.org/10.48550/arxiv.2605.26732"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.26732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26732","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.26732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137191690","display_name":"Yifan Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137179581","display_name":"Lei Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137100150","display_name":"Sijie Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Sijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137107089","display_name":"Ting Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100610173","display_name":"Jianlong Li","orcid":"https://orcid.org/0000-0002-8205-9334"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jianlong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137182477","display_name":"Shikai Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Shikai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.15569999814033508,"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"}},"topics":[{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.15569999814033508,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.1395999938249588,"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/T10822","display_name":"Acoustic Wave Phenomena Research","score":0.0786999985575676,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/amplitude","display_name":"Amplitude","score":0.6862000226974487},{"id":"https://openalex.org/keywords/phase","display_name":"Phase (matter)","score":0.5519999861717224},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5174999833106995},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.498199999332428},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.44670000672340393},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4332999885082245},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3889999985694885}],"concepts":[{"id":"https://openalex.org/C180205008","wikidata":"https://www.wikidata.org/wiki/Q159190","display_name":"Amplitude","level":2,"score":0.6862000226974487},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.498199999332428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49639999866485596},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.44670000672340393},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4332999885082245},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4000000059604645},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3889999985694885},{"id":"https://openalex.org/C81299745","wikidata":"https://www.wikidata.org/wiki/Q334269","display_name":"Transfer function","level":2,"score":0.3630000054836273},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3181999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31209999322891235},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C2777952282","wikidata":"https://www.wikidata.org/wiki/Q3354606","display_name":"Transfer operator","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.30079999566078186},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.26739999651908875},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.26732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26732","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,"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.26732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.26732","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,"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"},"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":{"Learning-based":[0],"surrogates":[1],"have":[2,14],"become":[3],"increasingly":[4],"effective":[5],"for":[6,96],"wave-field":[7,99],"prediction,":[8],"and":[9,87,148,161],"neural":[10,103],"operators":[11],"in":[12,33,110],"particular":[13],"shown":[15],"strong":[16],"performance":[17],"within":[18],"observed":[19],"frequency":[20,77],"regimes.":[21],"However,":[22],"higher-frequency":[23,37,98,134,174],"prediction":[24,109,175],"under":[25,136,165],"scarce":[26],"target":[27,133],"supervision":[28],"remains":[29,63],"comparatively":[30],"underexplored,":[31],"especially":[32],"wave":[34,178],"problems":[35],"where":[36],"data":[38],"are":[39],"substantially":[40],"more":[41,74],"expensive":[42],"to":[43],"simulate":[44],"or":[45],"measure":[46],"than":[47],"lower-frequency":[48,102],"data.":[49],"A":[50,101,126],"central":[51],"difficulty":[52],"is":[53,57],"that":[54,152,172],"cross-frequency":[55],"transfer":[56,186],"inherently":[58],"asymmetric:":[59],"coarse":[60,92,108,198],"amplitude":[61,120],"structure":[62,71,199],"relatively":[64],"stable":[65],"across":[66],"frequencies,":[67],"whereas":[68],"phase-sensitive":[69],"oscillatory":[70,177,205],"deteriorates":[72],"much":[73],"rapidly":[75],"as":[76,121],"increases.":[78],"Motivated":[79],"by":[80],"this":[81],"asymmetry,":[82],"we":[83,116],"propose":[84],"APEX,":[85],"Amplitude-anchored":[86],"Phase-prior-guided":[88],"Enhancement":[89],"from":[90,114],"eXtrapolated":[91],"predictions,":[93],"a":[94,107,122,140],"framework":[95],"target-scarce":[97],"prediction.":[100],"operator":[104],"first":[105],"provides":[106],"the":[111,119,132,137,188,203],"target-frequency":[112,167],"regime,":[113],"which":[115],"retain":[117],"only":[118],"transferable":[123,197],"structural":[124],"anchor.":[125],"conditional":[127],"flow-matching":[128],"enhancer":[129],"then":[130],"reconstructs":[131],"field":[135],"guidance":[138],"of":[139,176,187],"Green's-function-inspired":[141],"phase":[142],"prior.":[143],"Experiments":[144],"on":[145,183,194],"SimpleWave,":[146],"Helmholtz,":[147],"Maxwell":[149],"benchmarks":[150],"show":[151],"APEX":[153],"consistently":[154],"outperforms":[155],"direct":[156,184],"lower-to-higher":[157],"extrapolation,":[158],"target-adapted":[159],"operator,":[160],"joint":[162],"generative":[163],"baselines":[164],"limited":[166],"supervision.":[168],"Our":[169],"results":[170],"suggest":[171],"reliable":[173],"fields":[179],"should":[180],"not":[181],"rely":[182],"end-to-end":[185],"full":[189],"complex":[190],"field,":[191],"but":[192],"instead":[193],"explicitly":[195],"reusing":[196],"while":[200],"separately":[201],"recovering":[202],"missing":[204],"detail.":[206]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-28T00:00:00"}
