A graph-based framework using sequences of lobe dynamics constructs low-energy transfer trajectories in the Earth-Moon CR3BP, refines them via multiple shooting in the bicircular four-body problem, and demonstrates effectiveness against existing solutions.
Labr `eche, D
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 2polarities
background 2representative citing papers
SuNeRF-CME uses physics-informed NeRFs with ray-tracing for Thomson scattering and constraints on plasma continuity, direction, and speed to enable tomographic 3D reconstruction of CMEs from as few as two viewpoints, validated on synthetic data with low parameter errors.
A new aerocapture guidance method uses a probabilistic indicator function to estimate and mitigate failure risks, saving 71.43% to 100% of recoverable cases in high-uncertainty simulations across varied initial conditions and atmosphere models.
Geo-LoFTR is a geometry-aided deep learning model for map-based localization that outperforms prior methods under large illumination and scale variations on simulated and real Mars imagery.
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.
citing papers explorer
-
Design of low-energy transfers in cislunar space using sequences of lobe dynamics
A graph-based framework using sequences of lobe dynamics constructs low-energy transfer trajectories in the Earth-Moon CR3BP, refines them via multiple shooting in the bicircular four-body problem, and demonstrates effectiveness against existing solutions.
-
SuNeRF-CME: Physics-Informed Neural Radiance Fields for Tomographic Reconstruction of Coronal Mass Ejections
SuNeRF-CME uses physics-informed NeRFs with ray-tracing for Thomson scattering and constraints on plasma continuity, direction, and speed to enable tomographic 3D reconstruction of CMEs from as few as two viewpoints, validated on synthetic data with low parameter errors.
-
Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function
A new aerocapture guidance method uses a probabilistic indicator function to estimate and mitigate failure risks, saving 71.43% to 100% of recoverable cases in high-uncertainty simulations across varied initial conditions and atmosphere models.
-
Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions
Geo-LoFTR is a geometry-aided deep learning model for map-based localization that outperforms prior methods under large illumination and scale variations on simulated and real Mars imagery.
-
Machine Learning as a Transformative Tool for (Exo-)Planetary Science
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.