No abstract

The generalized Langevin equation (GLE) overcomes the limiting Markov approximation of the Langevin equation by an incorporated memory kernel and can be used to model various stochastic processes in many fields of science ranging from climate modeling over neuroscience to finance. Generally, Bayesian estimation facilitates the determination of both suitable model parameters and their credibility for a measured time series in a straightforward way. In this work we develop a realization of this estimation technique for the GLE in the case of white noise. We assume piecewise constant drift and diffusion functions and represent the characteristics of the data set by only a few coefficients, which leads to a numerically efficient procedure. The kernel function is an arbitrary time-discrete function with a fixed length K. We show how to determine a reasonable value of K based on the data. We illustrate the abilities of both the method and the model by an example from turbulence.

Many complex systems occurring in the natural or social sciences or economics are frequently described on a microscopic level, e.g., by lattice-or agent-based models. To analyse the solution and bifurcation structure of such systems on the level of macroscopic observables one has to rely on equation-free methods like stochastic continuation. Here, we investigate how to improve stochastic continuation techniques by adaptively choosing the model parameters. This allows one to obtain bifurcation diagrams quite accurately, especially near bifurcation points. We introduce lifting techniques which generate microscopic states with a naturally grown structure, which can be crucial for a reliable evaluation of macroscopic quantities. We show how to calculate fixed points of fluctuating functions by employing suitable linear fits. This procedure offers a simple measure of the statistical error. We demonstrate these improvements by applying the approach to give an analysis of (i) the Ising model in two dimensions, (ii) an active Ising model and (iii) a stochastic Swift-Hohenberg equation. We conclude by discussing the abilities and remaining problems of the technique.

Data-driven modeling of non-Markovian dynamics is a recent topic of research with applications in many fields such as climate research, molecular dynamics, biophysics, or wind power modeling. In the frequently used standard Langevin equation, memory effects can be implemented through an additional hidden component which functions as correlated noise, thus resulting in a non-Markovian model. It can