Sensitivity Approximation by the Peano-Baker Series

TL;DR

This paper introduces a new numerical method based on the Peano-Baker series for efficiently approximating sensitivities in parameter-dependent ODEs, reducing computational costs compared to traditional methods.

Contribution

The paper develops a novel sensitivity approximation technique using the Peano-Baker series, with proven error bounds and demonstrated efficiency improvements.

Findings

The method achieves an error scaling of O(Δt_max^2).

Numerical experiments show faster computation times than forward sensitivity analysis.

The approach is effective in systems biology and classical dynamical systems contexts.

Abstract

In this paper we develop a new method for numerically approximating sensitivities in parameter-dependent ordinary differential equations (ODEs). Our approach, intended for situations where the standard forward and adjoint sensitivity analyses become too computationally costly for practical purposes, is based on the Peano-Baker series from control theory. Using this series, we construct a representation of the sensitivity matrix $\mathbf{S}$ and, from this representation, a numerical method for approximating $\mathbf{S}$. We prove that, under standard regularity assumptions, the error of our method scales as $\mathcal{O}(\Delta t^2_{\text{max}})$, where $\Delta t_{\text{max}}$ is the largest time step used when numerically solving the ODE. We illustrate the performance of the method in several numerical experiments, taken from both the systems biology setting and more classical dynamical systems. The experiments show the sought-after improvement in running time of our method compared to the forward sensitivity approach. In experiments involving a random linear system, the forward approach requires roughly $\sqrt{n}$ longer computational time, where $n$ is the dimension of the parameter space, than our proposed method.