Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs
This work addresses parameter identification for researchers in fields like biology or engineering, offering incremental improvements in accuracy.
The paper tackled the challenge of parameter identification in nonlinear ODE systems by introducing a novel interpretation of Gaussian process-based gradient matching, resulting in improved accuracy for nonlinear dynamical systems.
Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system without explicitly solving it. While the benefits in computational cost are well established, a rigorous mathematical framework has been missing. We offer a novel interpretation which leads to a better understanding and improvements in state-of-the-art performance in terms of accuracy for nonlinear dynamical systems.