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Found 8 papers, 3 papers with code
NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling
We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations.
Computing low-thrust transfers in the asteroid belt, a comparison between astrodynamical manipulations and a machine learning approach
In this work, we propose new analytical approximations and compare their accuracy and performance to machine learning methods.
Closing the Gap Between SGP4 and High-Precision Propagation via Differentiable Programming
Thus, we propose a novel orbital propagation paradigm, ML-dSGP4, where neural networks are integrated into the orbital propagator.
High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery
Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO).
The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11
Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output.
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles
Solar radio flux along with geomagnetic indices are important indicators of solar activity and its effects.
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions.
Spacecraft Collision Risk Assessment with Probabilistic Programming
Over 34, 000 objects bigger than 10 cm in length are known to orbit Earth.
