{"paper":{"title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.geo-ph"],"primary_cat":"physics.space-ph","authors_text":"Ruochen Wang, Xiaoli Bai","submitted_at":"2026-07-31T23:43:50Z","abstract_excerpt":"Thermospheric mass density governs aerodynamic drag in low Earth orbit and is a primary source of uncertainty in orbit prediction and conjunction assessment, particularly during geomagnetic disturbances. We present AETHER-P3 (Accelerometer-driven Estimation of THERmospheric density-A Physics-Informed Probabilistic Prediction Platform), a machine-learning-based global thermospheric density forecasting model that provides multi-step forecasts up to 6 hr ahead using a 3-hr input window, with predictive uncertainty estimates. AETHER-P3 formulates thermospheric density forecasting as a sequence-to-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.00352","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2608.00352/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}