Generic local identifiability for ODE inverse problems from discrete observations

Abstract

We study local identifiability of parameters in ordinary differential equation models from finitely many observations. The central object is the parameter-to-observation map obtained by sampling the solution at prescribed times. We first prove a quantitative injectivity estimate for general $C^2$ observation maps: a lower bound on the smallest singular value of the parameter Jacobian, together with an upper bound on the second derivative, gives an explicit neighborhood on which the inverse problem has a unique and stable local solution. We then treat analytic ODE models. For analytic vector fields the observation map is analytic in observation times, initial states, and parameters; consequently, the loss of full parameter rank is contained in the zero set of a real analytic function. Under a single non-degeneracy condition this gives generic local identifiability, including for randomly chosen observation times with a density. Finally, for homogeneous linear systems $\dot{X}=AX$, we separate the recovery of $e^{hA}$ from the recovery of $A$: cyclic initial states identify the discrete propagator from one trajectory, while the remaining ambiguity is precisely the ambiguity of the real matrix logarithm.