{"id":"https://openalex.org/W7124316477","doi":"https://doi.org/10.48550/arxiv.2601.09491","title":"Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions","display_name":"Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions","publication_year":2026,"publication_date":"2026-01-14","ids":{"openalex":"https://openalex.org/W7124316477","doi":"https://doi.org/10.48550/arxiv.2601.09491"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.09491","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.09491","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.2601.09491","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123145501","display_name":"Beatrice Ceccanti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ceccanti, Beatrice","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103261572","display_name":"Mattia Galanti","orcid":"https://orcid.org/0000-0002-7544-0591"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Galanti, Mattia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123065623","display_name":"Ivo Roghair","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roghair, Ivo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123061121","display_name":"Martin van Sint Annaland","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Annaland, Martin van Sint","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.4674000144004822,"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":0.4674000144004822,"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":0.3346000015735626,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.01979999989271164,"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/generalization","display_name":"Generalization","score":0.6434999704360962},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.6341000199317932},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.609000027179718},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5756000280380249},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.536300003528595},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5174000263214111},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45730000734329224},{"id":"https://openalex.org/keywords/partial-differential-equation","display_name":"Partial differential equation","score":0.4560999870300293},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4359999895095825}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6434999704360962},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.6341000199317932},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.609000027179718},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5756000280380249},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49079999327659607},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4652000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C93779851","wikidata":"https://www.wikidata.org/wiki/Q271977","display_name":"Partial differential equation","level":2,"score":0.4560999870300293},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.45489999651908875},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3869999945163727},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C8171440","wikidata":"https://www.wikidata.org/wiki/Q903414","display_name":"Steady state (chemistry)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C2780799671","wikidata":"https://www.wikidata.org/wiki/Q17087362","display_name":"Transient (computer programming)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C150394285","wikidata":"https://www.wikidata.org/wiki/Q180254","display_name":"Adsorption","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.2870999872684479},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C78045399","wikidata":"https://www.wikidata.org/wiki/Q11214","display_name":"Differential equation","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.09491","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.09491","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.2601.09491","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.09491","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"Operator":[1],"Networks":[2],"are":[3,116,192],"emerging":[4],"as":[5,53,84,103,205,207,227],"fundamental":[6],"tools":[7],"among":[8],"various":[9],"neural":[10],"network":[11],"types":[12],"to":[13,25,28,60,80,96,183],"learn":[14],"mappings":[15],"between":[16],"function":[17],"spaces,":[18],"and":[19,91,180,220,236],"have":[20],"recently":[21],"gained":[22],"attention":[23],"due":[24],"their":[26,82],"ability":[27],"approximate":[29,184],"nonlinear":[30],"operators.":[31,188],"In":[32,65],"particular,":[33],"DeepONets":[34,70,182,226],"offer":[35],"a":[36,46,123,142,156,171],"natural":[37],"formulation":[38],"for":[39,77,86,159,231],"PDE":[40],"solving,":[41],"since":[42],"the":[43,72,129,133,185,199,222],"solution":[44,63,111,187],"of":[45,74,99,112,122,132,145,176],"partial":[47],"differential":[48],"equation":[49],"can":[50],"be":[51],"interpreted":[52],"an":[54,57],"operator":[55,160],"mapping":[56],"initial":[58,146,178,196],"condition":[59],"its":[61],"corresponding":[62,186],"field.":[64],"this":[66],"work,":[67],"we":[68,169],"applied":[69],"in":[71],"context":[73],"process":[75,89,126],"modeling":[76,138],"adsorption":[78,88,125,234],"technologies,":[79],"assess":[81],"feasibility":[83],"surrogates":[85,230],"cyclic":[87,100,124,233],"simulation":[90],"optimization.":[92],"The":[93,148,189,213],"goal":[94],"is":[95],"accelerate":[97],"convergence":[98],"processes":[101],"such":[102],"Temperature-Vacuum":[104],"Swing":[105],"Adsorption":[106],"(TVSA),":[107],"which":[108,115],"require":[109],"repeated":[110],"transient":[113],"PDEs,":[114],"computationally":[117],"expensive.":[118],"Since":[119],"each":[120],"step":[121],"starts":[127],"from":[128],"final":[130],"state":[131],"preceding":[134],"step,":[135],"effective":[136],"surrogate":[137],"requires":[139],"generalization":[140,165],"across":[141],"wide":[143],"range":[144],"conditions.":[147],"governing":[149],"equations":[150],"exhibit":[151],"steep":[152],"traveling":[153],"fronts,":[154],"providing":[155],"demanding":[157],"benchmark":[158],"learning.":[161],"To":[162],"evaluate":[163],"functional":[164,211],"under":[166],"these":[167],"conditions,":[168],"construct":[170],"mixed":[172],"training":[173,223],"dataset":[174],"composed":[175],"heterogeneous":[177],"conditions":[179,197],"train":[181],"trained":[190],"models":[191],"then":[193],"tested":[194],"on":[195,208],"outside":[198],"parameter":[200],"ranges":[201],"used":[202],"during":[203],"training,":[204],"well":[206],"completely":[209],"unseen":[210],"forms.":[212],"results":[214],"demonstrate":[215],"accurate":[216],"predictions":[217],"both":[218],"within":[219],"beyond":[221],"distribution,":[224],"highlighting":[225],"potential":[228],"efficient":[229],"accelerating":[232],"simulations":[235],"optimization":[237],"workflows.":[238]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-01-16T00:00:00"}
