{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Elizabeth Foughty", "hasEmail": "mailto:elizabeth.a.foughty@nasa.gov"}, "description": "ADAPTIVE MODEL REFINEMENT\r\nFOR THE IONOSPHERE AND THERMOSPHERE\r\n\r\nANTHONY M. D\u2019AMATO\u2217, AARON J. RIDLEY\u2217\u2217, AND DENNIS S. BERNSTEIN\u2217\u2217\u2217\r\n\r\nAbstract. Mathematical models of physical phenomena are of critical importance in\r\nvirtually all applications of science and technology. This paper addresses the problem of\r\nhow to use data to improve the fidelity of a given model. We approach this problem using\r\nretrospective cost optimization, a novel technique that uses data to recursively update an\r\nunknown subsystem interconnected to a known system. Applications of this research are\r\nrelevant to a wide range of applications that depend on large-scale models based on firstprinciples\r\nphysics, such as the Global Ionosphere-Thermosphere Model (GITM). Using\r\nGITM as the truth model, we demonstrate that measurements can be used to identify\r\nunknown physics. Specifically, we estimate static thermal conductivity parameters, and\r\nwe identify a dynamic cooling process.", "distribution": [{"@type": "dcat:Distribution", "description": "ADAPTIVE MODEL REFINEMENT FOR THE IONOSPHERE AND THERMOSPHERE", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Paper_18_.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Paper 18 .pdf"}], "identifier": "DASHLINK_240", "issued": "2010-10-13", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/240/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "ADAPTIVE MODEL REFINEMENT FOR THE IONOSPHERE AND THERMOSPHERE"}