NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling

TL;DR

This paper presents thermoNET, a neural network-based model for thermosphere density that integrates with NeuralODEs to improve satellite orbital propagation efficiency and accuracy using innovative training methods.

Contribution

Introducing thermoNET, a neural architecture that models thermosphere density within NeuralODEs, enabling efficient and accurate satellite orbit predictions with novel training approaches.

Findings

ThermoNET effectively models thermosphere density with reduced computations.

The NeuralODE framework allows for flexible, differentiable satellite dynamics modeling.

The approach improves numerical propagation efficiency while maintaining accuracy.

Abstract

We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations. Due to the appearance of a neural network on the right-hand side of the equations of motion, the resulting satellite dynamics is governed by a NeuralODE, a neural Ordinary Differential Equation, characterized by its fully differentiable nature, allowing the derivation of variational equations (hence of the state transition matrix) and facilitating its use in connection to advanced numerical techniques such as Taylor-based numerical propagation and differential algebraic techniques. Efficient training of the network parameters occurs through two distinct approaches. In the first approach, the network undergoes training independently of spacecraft dynamics, engaging in a pure regression task against ground truth models, including JB-08 and NRLMSISE-00. In the second paradigm, network parameters are learned based on observed dynamics, adapting through ODE sensitivities. In both cases, the outcome is a flexible, compact model of the thermosphere density greatly enhancing numerical propagation efficiency while maintaining accuracy in the orbital predictions.