Pointwise Minimax Vector Field Reconstruction from Noisy ODE

Abstract: This work addresses the problem of estimating a vector field from a noisy Ordinary Differential Equation (ODE) in a non-parametric regression setting with a random design for initial values. More specifically, given a vector field $ f:\mathbb{R}^{D}\rightarrow \mathbb{R}^{D}$ governing a dynamical system defined by the autonomous ODE: $y' = f(y)$, we assume that the observations are $\tilde{y}{X{i}}(t_{j}) = y_{X_{i}}(t_{j}) + \varepsilon_{i,j}$ where $y_{X_{i}}(t_{j})$ is the solution of the ODE at time $t_{j}$ with initial condition $y(0) = X_{i}$, $X_{i}$ is sampled from a probability distribution $\mu$, and $\varepsilon_{i,j}$ some noise. In this context, we investigate, from a minimax perspective, the pointwise reconstruction of $f$ within the envelope of trajectories originating from the support of $\mu$. We propose an estimation strategy based on preliminary flow reconstruction and techniques from derivative estimation in non-parametric regression. Under mild assumptions on $f$, we establish convergence rates that depend on the temporal resolution, the number of sampled initial values and the mass concentration of $\mu$. Importantly, we show that these rates are minimax optimal. Furthermore, we discuss the implications of our results in a manifold learning setting, providing insights into how our approach can mitigate the curse of dimensionality.