Algebraic parameter estimation improved for noisy dynamical systems
Practical Algebraic Parameter Estimation for Noisy Data via Gaussian Process Regression
Mathematical Software
Summary
Estimating parameters in systems described by ordinary differential equations can be hard, especially when the data is noisy. The authors show that using Gaussian Process Regression to estimate derivatives makes algebraic methods more practical for noisy data. This approach works well in many tested systems, achieving accurate parameter estimates without needing initial guesses. It combines robustness against noise with the mathematical benefits of algebraic formulations.
What this means in practice
- •For mechanical engineers: Improve parameter estimation accuracy in noisy measurements of dynamic mechanical systems without initial parameter guesses.
- •For systems biology modelers: Obtain reliable parameter values from noisy biological system data to better understand underlying biological processes.
Authors
Oren Bassik, Alexander Demin, Alexey Ovchinnikov
Abstract
Parameter estimation for ordinary differential equation (ODE) models is a fundamental task that is often complicated by the limitations of conventional optimization-based methods. In theory, differential-algebraic approaches offer an appealing alternative: they reduce the problem to polynomial system solving and do not require user-supplied initial guesses for parameter values. In practice, however, algebraic methods have been limited by their sensitivity to measurement noise, because they require accurate derivatives of observed outputs. In this work, we integrate Gaussian Process Regression (GPR) into the differential-algebraic method and derive a first-order error analysis in terms of noise level and algebraic sensitivity. We evaluate the method across several noise levels on a benchmark of 25 dynamical systems arising in applications including mechanical engineering and systems biology. The proposed method achieves the highest aggregate performance among the methods considered, recovering all sought parameter values and initial conditions to within 10% relative error in 88.5% of runs. These results demonstrate that robust derivative estimation can make differential-algebraic parameter estimation practical for dense, noisy synthetic data while retaining key advantages of the algebraic formulation.