Picard Iteration for Parameter Estimation in Nonlinear Ordinary Differential Equations using Low-Quality Data

We consider the problem of using experimental time-series data for parameter estimation in nonlinear ordinary differential equations, focusing on the case where the data is noisy, sparse, irregularly sampled, includes multiple experiments, and does not directly measure the system state or its time derivative.

To account for such low-quality data, we propose a new framework for gradient-based parameter estimation which uses the Picard operator to reformulate the problem as constrained optimization with infinite-dimensional variables and constraints.

We then formulate the Karush-Kuhn-Tucker (KKT) conditions necessary for optimality and define a convergent sequence of approximations to these KKT conditions obtained by replacing the solution map by the n-th order Picard iterate.

Then, for any element of this sequence, and by exploiting the contractive properties of the Picard operator, we propose a gradient-contractive algorithm which (under regularity and convexity assumptions) is guaranteed to converge to a solution of these approximated KKT conditions.

Finally, the algorithms are then tested on a battery of models and a variety of datasets in order to demonstrate robustness and improvement over alternative approaches.