Finding Commonalities in Dynamical Systems with Gaussian Processes

Abstract

Gaussian processes can be utilized in the area of equation discovery to identify differential equations describing the physical processes present in time series data.
Furthermore, automatically constructed models can be split into components that facilitate comparisons between time series on a structural level. We consider the potential combination of these two methods and describe how they could be used to detect shared physical properties in multiple recordings of dynamical systems as time series. This approach provides insights into the underlying dynamics of the observed systems, facilitating a deeper understanding of complex processes.