A graph neural network recommends combinatorial choices for tether-net morphology, masses, thrusters, and aiming points, reducing the MCNLP to an NLP solved by PSO and yielding faster convergence than direct optimization.
Concurrent design optimization of tether-net system and actions for reliable space-debris capture,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
An EFGN learns directed improvement graphs over combinatorial spaces to act as a recommender for MCNLP solvers, yielding better optima than index-based baselines when paired with PSO and GA on benchmarks.
Presents a UQ pipeline applying Sobol sensitivity analysis and perturbation methods to quantify noisy-observation effects on Capture Quality Index for fixed-control and neuro-controlled active tether-net systems, using high- and low-fidelity simulators.
citing papers explorer
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Designing Active Tether-Net Systems for Space Debris Capture with Graph-Learning-Aided Mixed-Combinatorial Optimization
A graph neural network recommends combinatorial choices for tether-net morphology, masses, thrusters, and aiming points, reducing the MCNLP to an NLP solved by PSO and yielding faster convergence than direct optimization.
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Learning-based Directed Graph Abstraction of Combinatorial Spaces for Order-Preserving Search in Mixed-Combinatorial Nonlinear Optimization
An EFGN learns directed improvement graphs over combinatorial spaces to act as a recommender for MCNLP solvers, yielding better optima than index-based baselines when paired with PSO and GA on benchmarks.
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Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations
Presents a UQ pipeline applying Sobol sensitivity analysis and perturbation methods to quantify noisy-observation effects on Capture Quality Index for fixed-control and neuro-controlled active tether-net systems, using high- and low-fidelity simulators.