{"id":"W4389116611","doi":"10.48550/arxiv.2311.16080","title":"XLB: A differentiable massively parallel lattice Boltzmann library in Python","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"Nvidia","keywords":"Python (programming language); Computer science; Scalability; Lattice Boltzmann methods; Differentiable function; Computational science; Massively parallel; Parallel computing; Lattice (music); Extensibility; Distributed computing; Programming language; Operating system; Physics; Mathematics; Mechanics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001130204,0.0005127452,0.000608082,0.0005791447,0.00009784332,0.0001071363,0.0006478081,0.0004254034,0.0001348684],"category_scores_gemma":[0.00003441631,0.0006254963,0.0002126591,0.00077487,0.0000839497,0.0005142444,0.001312946,0.000894657,0.0006315617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000206457,"about_ca_system_score_gemma":0.00006080915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007391845,"about_ca_topic_score_gemma":0.00009132426,"domain_scores_codex":[0.9980104,0.00009422191,0.000366555,0.0008380476,0.0001205422,0.0005702277],"domain_scores_gemma":[0.9986644,0.0002573747,0.0001249279,0.0007537302,0.0000507589,0.0001487596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003783972,0.00004688562,0.03050834,0.0003947134,0.0002445412,0.0004489373,0.0003102113,0.9556096,0.0000119139,0.00833895,0.004011969,0.00003609313],"study_design_scores_gemma":[0.001383443,0.00002264582,0.1010156,0.0004135304,0.000218557,0.000001507494,0.0004437748,0.8548054,0.00004481747,0.03629718,0.004197758,0.001155811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579624,0.0004501252,0.02074322,0.0001886838,0.001191028,0.0007842162,0.0001353413,0.003435098,0.01510991],"genre_scores_gemma":[0.982237,0.0009686202,0.0005556627,0.00004171011,0.0001235403,0.000006621196,0.0001034594,0.0001726475,0.01579071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1008043,"threshold_uncertainty_score":0.9996197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08186107162551268,"score_gpt":0.1904685934104837,"score_spread":0.1086075217849711,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}