Derives saddle-point optimal guidance laws for low-thrust orbital pursuit-evasion that achieve arbitrary terminal relative speeds via cost-function weighting in an LQ zero-sum game.
A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement
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A distribution-agnostic robust trajectory optimization framework uses chance-constrained reinforcement learning with rollout-based quantiles to enforce probabilistic feasibility on nominal trajectories via affine corrections.
Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
citing papers explorer
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Low-Thrust Orbital Differential Games with Speed Constraint Enforcement Using Cost Weighting
Derives saddle-point optimal guidance laws for low-thrust orbital pursuit-evasion that achieve arbitrary terminal relative speeds via cost-function weighting in an LQ zero-sum game.
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Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
A distribution-agnostic robust trajectory optimization framework uses chance-constrained reinforcement learning with rollout-based quantiles to enforce probabilistic feasibility on nominal trajectories via affine corrections.
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Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability
Neural surrogates trained with scaling laws and self-similar transformations accurately approximate low-thrust trajectory costs and reachability while generalizing across orbital parameters.
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MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
MoTIF uses HOSVD to separate multi-parametric unsteady flow data into modal components, applies GPR for parametric and spatial interpolation and RNN for temporal forecasting, achieving under 2% relative RMS error on laminar flow cases with varying Reynolds number and angle of attack.
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