# Machine-Learned HASDM Model with Uncertainty Quantification

@article{Licata2021MachineLearnedHM, title={Machine-Learned HASDM Model with Uncertainty Quantification}, author={Richard Joseph Licata and Piyush M. Mehta and W. Kent Tobiska and Snehalata V. Huzurbazar}, journal={ArXiv}, year={2021}, volume={abs/2109.07651} }

The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Space Environment Technologies (SET), contains 20 years of outputs from the U.S. Space Force’s High Accuracy Satellite Drag Model (HASDM), which represents the state-of-the-art for density and drag modeling. We utilize principal component analysis (PCA) for dimensionality reduction, creating the coefficients upon which…

## 2 Citations

### Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application

- Environmental ScienceArXiv
- 2022

Two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates are proposed: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function.

### A Framework to Estimate Local Atmospheric Densities With Reduced Drag‐Coefficient Biases

- Environmental ScienceSpace Weather
- 2022

An accurate estimation of upper atmospheric densities is crucial for precise orbit determination (POD), prediction of low Earth orbit satellites, and scientific studies of the Earth's atmosphere. But…

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