From d8abda2730f1f4a5f24b9a13e7fb209f03afa2c6 Mon Sep 17 00:00:00 2001 From: Giacomo Acciarini Date: Fri, 8 Nov 2024 09:28:23 +0100 Subject: [PATCH] Update README.md --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b4b89ad..6dd3059 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,12 @@ Differentiable SGP4. ![orbits](https://github.com/esa/dSGP4/assets/33602846/2f42992d-0838-4c11-ae4b-68ad76e2bf33) -This repository contains the code discussed in [this paper](https://arxiv.org/abs/2402.04830). $\partial \textrm{SGP4}$ is a differentiable version of SGP4 implemented using PyTorch. By making SGP4 differentiable, $\partial \textrm{SGP4}$ facilitates various space-related applications, including spacecraft orbit determination, covariance transformation, state transition matrix computation, and covariance propagation. Additionally, $\partial \textrm{SGP4}$'s PyTorch implementation allows for parallel orbital propagation across batches of Two-Line Element Sets (TLEs), leveraging the computational power of CPUs, GPUs, and advanced hardware for distributed prediction of satellite positions at future times. Furthermore, $\partial \textrm{SGP4}$'s differentiability enables integration with modern machine learning techniques. Thus, we propose a novel orbital propagation paradigm, $\textrm{ML}-\partial \textrm{SGP4}$, where neural networks are integrated into the orbital propagator. Through stochastic gradient descent, this combined model's inputs, outputs, and parameters can be iteratively refined, surpassing SGP4's precision while maintaining computational speed. This empowers satellite operators and researchers to train the model using high-precision simulated or observed data, advancing orbital prediction capabilities compared to the standard SGP4. +This repository contains the code discussed in [this paper](https://doi.org/10.1016/j.actaastro.2024.10.063). + +$\partial \textrm{SGP4}$ is a differentiable version of SGP4 implemented using PyTorch. By making SGP4 differentiable, $\partial \textrm{SGP4}$ facilitates various space-related applications, including spacecraft orbit determination, covariance transformation, state transition matrix computation, and covariance propagation. +Additionally, $\partial \textrm{SGP4}$'s PyTorch implementation allows for parallel orbital propagation across batches of Two-Line Element Sets (TLEs), leveraging the computational power of CPUs, GPUs, and advanced hardware for distributed prediction of satellite positions at future times. Furthermore, $\partial \textrm{SGP4}$'s differentiability enables integration with modern machine learning techniques. +Thus, we propose a novel orbital propagation paradigm, $\textrm{ML}-\partial \textrm{SGP4}$, where neural networks are integrated into the orbital propagator. +Through stochastic gradient descent, this combined model's inputs, outputs, and parameters can be iteratively refined, surpassing SGP4's precision while maintaining computational speed. This empowers satellite operators and researchers to train the model using high-precision simulated or observed data, advancing orbital prediction capabilities compared to the standard SGP4. ## Goals