Mathematical Data Science in the Ionosphere
Abstract
The proposed work will produce algorithms to obtain lightweight, data-driven forecasting models of plasma density in the ionosphere given a time series of observations. The proposed effort will leverage recently published work by the PI and Co-PIs on methods to fit dynamical models from data measurements exhibiting complex, nonlinear, and sometimes multiscale behavior. We will achieve this by merging what we have learned using modal decomposition and deep learning along with information theory and nonparametric statistics to yield improved short-term forecasts over the current state-of-the-art. This effort will consist of two major aims. In Aim I, we will focus on generating machine learning forecasts for ionospheric specification via deep-learning-based dynamic mode decomposition. Aim II will then characterize the spatiotemporal distributions of fluctuations in the ionosphere not captured by our forecast using nonparametric statistical methods. Together, these two aims form a complete, fully data-driven characterization of the local ionospheric profile dynamics and the error signal
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 03, 2023
- Source ID
- N000142312106
Entities
People
- Christopher W. Curtis
Organizations
- Office of Naval Research
- Salk Institute for Biological Studies
- United States Navy