A Bayesian inference approach with Metropolis-Hastings sampling learns continuous-time dynamics from sparse measurements to enable uncertainty-aware scenario optimal control.
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Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control
A Bayesian inference approach with Metropolis-Hastings sampling learns continuous-time dynamics from sparse measurements to enable uncertainty-aware scenario optimal control.