# Hybrid modeling and prediction of dynamical systems

## TL;DR

This paper introduces a hybrid modeling approach that combines mechanistic and nonparametric methods to improve prediction robustness and parameter estimation in dynamical systems, especially under uncertainty.

## Contribution

It proposes replacing parts of mechanistic models with nonparametric representations, enhancing prediction accuracy and robustness in chaotic and neural systems.

## Key findings

- Hybrid models outperform purely mechanistic models in uncertain conditions.
- Improved short-term prediction accuracy demonstrated on Lorenz-63 and neural network data.
- Hybrid approach offers better parameter estimation in noisy, real-world data.

## Abstract

Scientific analysis often relies on the ability to make accurate predictions of a system's dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model's equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data.