Conference on Intelligent Data Understanding 2010 (CIDU)

ADAPTIVE MODEL REFINEMENT
FOR THE IONOSPHERE AND THERMOSPHERE

ANTHONY M. D’AMATO∗, AARON J. RIDLEY∗∗, AND DENNIS S. BERNSTEIN∗∗∗

Abstract. 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.