# Reduced Order Probabilistic Emulation for Physics-Based Thermosphere Models

arXiv: https://arxiv.org/abs/2211.04392 · HTML: https://mlsanity.com/paper/2211.04392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Dynamic mode decomposition with control](https://mlsanity.com/paper/1409.6358/) (2014)
- [Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging](https://mlsanity.com/paper/2104.12376/) (2021)
- [Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction](https://mlsanity.com/paper/1808.03867/) (2018)
- [Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling](https://mlsanity.com/paper/2206.05824/) (2022)
- [Calibrated and Enhanced NRLMSIS 2.0 Model with Uncertainty Quantification](https://mlsanity.com/paper/2208.11619/) (2022)

## Built on by (most cited first, 1 of 1)

- [Probabilistic Solar Proxy Forecasting with Neural Network Ensembles](https://mlsanity.com/paper/2306.02169/) (2023)

## Abstract (arXiv)

The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a physics-based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long-Short Term Memory neural networks to perform time-series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE-GCM ROPE has similar error to previous linear approaches while improving storm-time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density with < 5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7 - 18 km.