{"id":"https://openalex.org/W7169867101","doi":"https://doi.org/10.48550/arxiv.2607.18020","title":"FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers","display_name":"FlashPDE: A Drop-In Fused Triton Operator Library for Neural PDE Solvers","publication_year":2026,"publication_date":"2026-07-20","ids":{"openalex":"https://openalex.org/W7169867101","doi":"https://doi.org/10.48550/arxiv.2607.18020"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.18020","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18020","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":null,"license_id":null,"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.2607.18020","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000528868","display_name":"Peiyu Zang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zang, Peiyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5141287907","display_name":"Bosen Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Bosen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133145909","display_name":"Ruoxiang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ruoxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5141301899","display_name":"Yongqiang Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yongqiang","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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9767000079154968,"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":0.9767000079154968,"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/T10054","display_name":"Parallel Computing and Optimization Techniques","score":0.0031999999191612005,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T11697","display_name":"Numerical Methods and Algorithms","score":0.002099999925121665,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/speedup","display_name":"Speedup","score":0.7013999819755554},{"id":"https://openalex.org/keywords/stencil","display_name":"Stencil","score":0.6421999931335449},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.6165000200271606},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.5633999705314636},{"id":"https://openalex.org/keywords/automatic-differentiation","display_name":"Automatic differentiation","score":0.5608999729156494},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5569999814033508},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5303999781608582},{"id":"https://openalex.org/keywords/cuda","display_name":"CUDA","score":0.516700029373169}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7451000213623047},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.7013999819755554},{"id":"https://openalex.org/C76752949","wikidata":"https://www.wikidata.org/wiki/Q7607499","display_name":"Stencil","level":2,"score":0.6421999931335449},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.6309000253677368},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.6165000200271606},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.5633999705314636},{"id":"https://openalex.org/C133512626","wikidata":"https://www.wikidata.org/wiki/Q787371","display_name":"Automatic differentiation","level":3,"score":0.5608999729156494},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5303999781608582},{"id":"https://openalex.org/C2778119891","wikidata":"https://www.wikidata.org/wiki/Q477690","display_name":"CUDA","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.43970000743865967},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3847000002861023},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2842999994754791},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C150495011","wikidata":"https://www.wikidata.org/wiki/Q128392","display_name":"Concurrent computing","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C128099668","wikidata":"https://www.wikidata.org/wiki/Q573952","display_name":"Lazy evaluation","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.26190000772476196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.18020","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18020","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.18020","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18020","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Physics-Informed":[0],"Neural":[1],"Networks":[2],"(PINNs)":[3],"solve":[4],"PDEs":[5],"by":[6,18,112,126],"incorporating":[7],"physical":[8],"constraints":[9],"into":[10],"neural-network":[11],"training,":[12],"but":[13],"large-scale":[14],"problems":[15],"are":[16],"limited":[17],"automatic-differentiation":[19],"memory":[20,110],"overhead":[21],"and":[22,63,87,96,121,149,174],"inefficient":[23],"execution":[24,47,167],"of":[25,93],"grid-based":[26,38],"PDE":[27,78,139,172],"operators.":[28],"We":[29],"present":[30],"FlashPDE,":[31],"a":[32,67,165],"drop-in":[33],"fused":[34,55],"operator":[35,53],"library":[36,74],"for":[37],"scientific":[39],"machine":[40],"learning.":[41],"FlashPDE":[42,107,141,163],"replaces":[43],"fragmented":[44],"PyTorch":[45,69,133,160,180],"finite-difference":[46,134,161],"with":[48,117,131,159],"differentiable":[49,77,171],"Triton":[50],"kernels.":[51],"Each":[52],"integrates":[54],"stencil":[56],"evaluation,":[57],"an":[58,101],"analytic":[59],"discrete-adjoint":[60],"backward":[61],"pass,":[62],"boundary-gradient":[64],"correction":[65],"within":[66,178],"unified":[68],"autograd":[70],"Function":[71],"interface.":[72],"The":[73],"provides":[75,164],"14":[76],"operators":[79],"covering":[80],"17":[81],"configurations":[82],"across":[83],"1D--3D":[84],"elliptic,":[85],"parabolic,":[86],"Navier--Stokes":[88],"systems,":[89],"while":[90,155],"remaining":[91],"independent":[92],"neural":[94],"architectures":[95],"training":[97],"strategies.":[98],"Experiments":[99],"on":[100],"NVIDIA":[102],"A100":[103],"GPU":[104],"show":[105],"that":[106,169],"reduces":[108,122],"peak":[109],"usage":[111],"up":[113,127,143,150],"to":[114,128,144,151],"37.0x":[115],"compared":[116,130],"coordinate-based":[118],"automatic":[119],"differentiation":[120],"CUDA":[123],"kernel":[124],"launches":[125],"3.5x":[129],"eager":[132],"implementations.":[135],"Across":[136],"six":[137],"representative":[138],"benchmarks,":[140],"achieves":[142],"2.30x":[145],"end-to-end":[146],"time-to-solution":[147],"speedup":[148],"19.2x":[152],"kernel-level":[153],"acceleration":[154],"maintaining":[156],"numerical":[157,176],"agreement":[158],"references.":[162],"hardware-efficient":[166],"layer":[168],"bridges":[170],"solvers":[173],"GPU-optimized":[175],"computation":[177],"the":[179],"ecosystem.":[181]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-22T00:00:00"}