Adaptive Model Refinement for the Ionosphere and Thermosphere

Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, a novel technique that uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this research are relevant to a wide range of applications that depend on large-scale models based on firstprinciples physics, such as the Global Ionosphere-Thermosphere Model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, and we identify a dynamic cooling process.

• Type: Journal
• Year: 2010
• Where: CIDU
• Authors: Anthony M. D'Amato, Aaron J. Ridley, Dennis S. Bernstein
• Keywords: CIDU 2010, Global Ionosphere-Thermosphere Model, Machine Learning, Model, Static Thermal Conductivity