Abstract: Identifying causal drivers in multivariate time-series data is central to finance, climate science, and other domains where interactions are nonlinear, high-dimensional, and noisy. Standard ...
Abstract: This letter proposes a novel Data-Driven (DD) method for controlling unknown input-affine nonlinear systems. First, we estimate the system dynamics from noisy data offline through Subspace ...
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