Summary of Closing the Gap Between Sgp4 and High-precision Propagation Via Differentiable Programming, by Giacomo Acciarini et al.
Closing the Gap Between SGP4 and High-Precision Propagation via Differentiable Programming
by Giacomo Acciarini, Atılım Güneş Baydin, Dario Izzo
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Earth and Planetary Astrophysics (astro-ph.EP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dSGP4 method is a differentiable version of the widely used Simplified General Perturbations 4 (SGP4) orbital propagation method. By making SGP4 differentiable, dSGP4 enables various space-related applications, including orbit determination, state conversion, and covariance propagation. The PyTorch implementation allows for parallelized orbital propagation across batches of Two-Line Element Sets (TLEs), leveraging the computational power of CPUs, GPUs, and advanced hardware. Additionally, the differentiability enables integration with modern machine learning techniques. A novel paradigm, ML-dSGP4, combines neural networks with the orbital propagator, iteratively refining inputs, outputs, and parameters through stochastic gradient descent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict where satellites will be in space. The method is called dSGP4 and it’s an upgrade to a popular existing method called SGP4. The new method makes it easier to use machine learning to improve satellite predictions. This can help satellite operators and researchers make more accurate predictions about where satellites will be in the future. |
Keywords
* Artificial intelligence * Machine learning * Stochastic gradient descent
