2 citations · 4 across the 7 of their papers we have counts for
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Improving Computational Efficiency for Powered Descent Guidance via Transformer-based Tight Constraint Prediction
Julia Briden, Trey Gurga, Breanna Johnson +2
In this work, we present Transformer-based Powered Descent Guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation o…
Constraint-Informed Learning for Warm Starting Trajectory Optimization
Julia Briden, Changrak Choi, Kyongsik Yun +2
Future spacecraft and surface robotic missions require increasingly capable autonomy stacks for exploring challenging and unstructured domains, and trajectory optimization will be…
Transformer-based Atmospheric Density Forecasting
Julia Briden, Peng Mun Siew, Victor Rodriguez-Fernandez +1
As the peak of the solar cycle approaches in 2025 and the ability of a single geomagnetic storm to significantly alter the orbit of Resident Space Objects (RSOs), techniques for at…