How can we learn the laws underlying the dynamics of stochastic systems when their trajectories are sampled sparsely in time? Existing methods either require temporally resolved high-frequency observa...
5 articles tagged "math-DS" — page 1 of 1
From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints [TOP LAB](arxiv.org)
|paper|arXiv
From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints [TOP LAB](arxiv.org)
|paper|arXiv
How can we learn the laws underlying the dynamics of stochastic systems when their trajectories are sampled sparsely in time? Existing methods either require temporally resolved high-frequency observa...
Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control [TOP LAB](arxiv.org)
|paper|arXiv
Neural network controllers increasingly demand millions of parameters, and language model approaches push into the billions. For embedded aerospace systems with strict power and latency constraints, t...
Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control [TOP LAB](arxiv.org)
|paper|arXiv
Neural network controllers increasingly demand millions of parameters, and language model approaches push into the billions. For embedded aerospace systems with strict power and latency constraints, t...