Oliver Kamps

• PhD
• University of Münster

The quantitative formulation of evolution equations is the backbone for prediction, control, and understanding of dynamical systems across diverse scientific fields. Besides deriving differential equations for dynamical systems based on basic scientific reasoning or prior knowledge in recent times a growing interest emerged to infer these equations...

The Greenland Ice Sheet may be nearing a tipping point, transitioning to permanent melting. This article analyses two melt rate time series using the Bayesian Langevin estimation (BLE), providing further evidence for destabilizing melt dynamics, along with new insights from the method's nonlinear parameterisation. Comparing the results for Western...

Granger causality can uncover the cause-and-effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz–Hodge–Kodaira decomposition can split them into rotational and gradient components which reveal the hierarchy of the Granger causality flow. Using Kenneth French’s business se...

Granger causality can uncover the cause and effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger causality flow. Using Kenneth French's business sect...

Cooperation between individuals is emergent in all parts of society; yet, mechanistic reasons for this emergence are ill understood in the literature. A specific example of this is insurance. Recent work has, though, shown that assuming the risk individuals face is proportional to their wealth and optimizing the time average growth rate rather than...

The geometric Brownian motion (GBM) is a standard model in quantitative finance, but the potential function of its stochastic differential equation (SDE) cannot include stable nonzero prices. This article generalizes the GBM to an SDE with polynomial drift of order q and shows via model selection that q = 2 is most frequently the optimal model to d...

Critical transitions, ubiquitous in nature and technology, necessitate anticipation to avert adverse outcomes. While many studies focus on bifurcation-induced tipping, where a control parameter change leads to destabilization, alternative scenarios are conceivable, e.g. noise-induced tipping by an increasing noise level in a multi-stable system. Al...

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extr...

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the da...

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract...

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500 which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the dat...

Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of dist...

Mitigating climate change requires a transition away from fossil fuels towards renewable energy. As a result, power generation becomes more volatile and options for microgrids and islanded power-grid operation are being broadly discussed. Therefore, studying the power grids of physical islands, as a model for islanded microgrids, is of particular i...

Critical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However, no effective method allows to establish practic...

Early warning indicators often suffer from the shortness and coarse-graining of real-world time series. Furthermore, the typically strong and correlated noise contributions in real applications are severe drawbacks for statistical measures. Even under favourable simulation conditions the measures are of limited capacity due to their qualitative nat...

Critical transitions are ubiquitous in nature and technology. Their anticipation is important to prevent unfavourable changes like outages in power grids or regime shifts in ecology, climatology and much more. Most studies focus on the anticipation of bifurcation-induced tipping which means destabilisation by change of a control parameter. However,...

Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of dist...

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 nois...

The design of reliable indicators to anticipate critical transitions in complex systems is an important task in order to detect a coming sudden regime shift and to take action in order to either prevent it or mitigate its consequences. We present a data-driven method based on the estimation of a parameterized nonlinear stochastic differential equat...

Commonly proposed statistical early warning measures are far away from realistic applications in which limited data availability, coarse-grained sampling and strong correlated noise are typical. Even under favourable simulation conditions the measures are of limited capacity due to their qualitative nature and sometimes ambiguous trend-to-noise rat...

A critical transition can occur in many real-world systems and the ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWS) have been developed to anticipate a critical transition or distinguish types of transitions. However, there is no effective method to establish practical...

The design of reliable indicators to anticipate critical transitions in complex systems is an im portant task in order to detect a coming sudden regime shift and to take action in order to either prevent it or mitigate its consequences. We present a data-driven method based on the estimation of a parameterized nonlinear stochastic differential equa...

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...

Langevin models are widely used to model various stochastic processes in different fields of natural and social sciences. They are adapted to measured data by estimation techniques such as maximum likelihood estimation, Markov chain Monte Carlo methods, or the non-parametric direct estimation method introduced by Friedrich et al. (Phys Lett A 271(3...

Langevin models are frequently used to model various stochastic processes in different fields of natural and social sciences. They are adapted to measured data by estimation techniques such as maximum likelihood estimation, Markov chain Monte Carlo methods, or the non-parametric direct estimation method introduced by Friedrich et al. The latter has...

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 analyze the states of such systems and their bifurcation structure on the level of macroscopic observables, one has to rely on equation-free methods like stochastic continuation....

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,...

In future power systems, electrical storage will be the key technology for balancing feed-in fluctuations. With increasing share of renewables and reduction of system inertia, the focus of research expands toward short-term grid dynamics and collective phenomena. Against this backdrop, Kuramoto-like power grids have been established as a sound math...

Stochastic feed-in of fluctuating renewable energies is steadily increasing in modern electricity grids, and this becomes an important risk factor for maintaining power grid stability. Here, we study the impact of wind power feed-in on the short-term frequency fluctuations in power grids based on an Institute of Electrical and Electronics Engineers...

We study the impact of stochastic wind power feed-in on the short-term frequency fluctuations in power grids based on an IEEE test grid structure, the swing equation for the dynamics of voltage phase angles, and a series of measured wind speed data. External control measures are accounted for by adjusting the grid state to the average power feed-in...

In future power systems, electrical storage will be the key technology for balancing feed-in fluctuations. With increasing share of renewables and reduction of system inertia, the focus of research expands towards short-term grid dynamics and collective phenomena. Against this backdrop, Kuramoto-like power grids have been established as a sound mat...

In this article we study power grids from the viewpoint of Synergetics. We show that the typical behavior of self-organizing systems like phase transitions and critical fluctuations can be observed in models for the dynamics of power grids. Therefore we numerically investigate a model, where the phase and voltage dynamics are represented by Kuramot...

Fluctuating wind energy makes a stable grid operation challenging. Due to the direct contact with atmospheric turbulence, intermittent short-term variations in the wind speed are converted to power fluctuations that cause transient imbalances in the grid. We investigate the impact of wind energy feed-in on short-term fluctuations in the frequency o...

Fluctuating wind energy makes a stable grid operation challenging. Due to the direct contact with atmospheric turbulence, intermittent short-term variations in the wind speed are converted to power fluctuations that cause transient imbalances in the grid. We investigate the impact of wind energy feed-in on short-term fluctuations in the frequency o...

Feed-in fluctuations induced by renewables are one of the key challenges to the stability and quality of electrical power grids. In particular short-term fluctuations disturb the system on a time scale, on which load balancing does not operate yet and the system is intrinsically governed by self-organized synchronization. Wind and solar power are k...

Feed-in fluctuations induced by renewables are one of the key challenges to the stability and quality of electrical power grids. In particular short-term fluctuations disturb the system on a time scale, on which load balancing does not operate yet and the system is intrinsically governed by self-organized synchronization. Wind and solar power are k...

In this article we discuss an extension of a method to extract Langevin equations from noisy time series to spatio-temporal data governed by stochastic partial differential equations (SPDEs). The reconstruction of the SPDEs from data is traced back to the estimation of multivariate conditional moments.

In this article we review two different approaches to the statistical description of turbulent flows in terms of evolution equations for probability density functions (PDFs), namely a description of the turbulent cascade by a Fokker- Planck equation, as well as kinetic equations in terms of the theoretical framework of the Lundgren-Monin-Novikov hi...

The fluctuations of electrical energy, generated by wind turbines, reflect the interaction between the turbulent wind field and a complex technical system. In this article we study time series of the integrated wind power production of a large number of spatially distributed wind energy converters. The aim is to characterize the fluctuations of win...

The integration of renewable energy sources in the course of the energy transition is accompanied by grid decentralization and fluctuating power feed-in characteristics. This raises new challenges for power system stability and design. We intend to investigate power system stability from the viewpoint of self-organized synchronization aspects. In t...

A statistical analysis of the two-point vorticity statistics in the inverse energy cascade of two-dimensional turbulence is presented in terms of probability density functions (PDFs). Evolution equations for the PDFs are derived in the framework of the Lundgren–Monin–Novikov hierarchy, and the unclosed terms are studied with the help of direct nume...

We present an overview of recent works on the statistical description of turbulent flows in terms of probability density functions (PDFs) in the framework of the Lundgren-Monin-Novikov (LMN) hierarchy. Within this framework, evolution equations for the PDFs are derived from the basic equations of fluid motion. The closure problem arises either in t...

In this paper we report on a comparison of Lagrangian acceleration statistics in the direct energy cascade of three-dimensional turbulence and the corresponding observables for the case of the inverse energy cascade in two dimensions. We focus on the time scales describing the memory of the acceleration statistics of a tracer particle. We show that...

In this talk we present results from the analysis of the Lagrangian acceleration statistics in curvilinear coordinates. The investigation is based on numerical simulations of isotropic, homogeneous turbulence in three dimensions for a certain range of Reynolds numbers. We focus on the effect of curvilinear coordinates on statistical observables lik...

The problem of relating Lagrangian and Eulerian statistics is a long standing problem in basic and applied turbulence research. Motivated by the investigation of Lagrangian statistics in the inverse cascade regime of 2D turbulence and in fully developed 3D Turbulence we adress the question of relating Lagrangian and Eulerian velocity increment stat...

We present measurements of conditional probability density functions (PDFs) that allow one to systematically bridge from Eulerian to Lagrangian statistics in fully developed 3D turbulence. The transition is investigated for hydro- as well as magnetohydrodynamic flows and comparisons are drawn. Significant differences in the transition PDFs are obse...

We present a formal connection between Lagrangian and Eulerian velocity increment distributions which is applicable to a wide range of turbulent systems ranging from turbulence in incompressible fluids to magnetohydrodynamic turbulence. For the case of the inverse cascade regime of two-dimensional turbulence we numerically estimate the transition p...

We investigate the relationship between Eulerian and Lagrangian probability density functions obtained from numerical simulations of two-dimensional as well as three-dimensional turbulence. We show that in contrast to the structure functions of the Lagrangian velocity increment δ τ v(y) = u(x(y, τ), τ) − u(y, 0), where u(x, t) denotes the Eulerian...

The multiple-point probability density f(v1, r1; v2, r2; ...vN, rN ) of velocity increments vi at different length scales ri is investigated in a direct numerical simulation of two-dimensional turbulence. It has been shown for experimental data of three-dimensional turbulence, that this probability density can be represented by conditional probabil...

We report on simulations of two-dimensional turbulence in the inverse energy cascade regime. Focusing on the statistics of Lagrangian tracer particles, scaling behavior of the probability density functions of velocity fluctuations is investigated. The results are compared to the three-dimensional case. In particular an analysis in terms of compensa...

In recent years it has been shown that by exploiting the Markovian properties of the velocity increments it is possible to extremely reduce the information necessary to describe the Eulerian velocity statistics. This approach, motivated by the theory of stochastic processes, is even more natural to the Lagrangian view of turbulence. Nevertheless a...

We present a numerical investigation of two-dimensional decaying turbulence in the Lagrangian framework. Focusing on single particle statistics, we investigate Lagrangian trajectories in a freely evolving turbulent velocity field. The dynamical evolution of the tracer particles is strongly dominated by the emergence and evolution of coherent struct...

In Lagrangian turbulence one is faced with the puzzle that 2D Navier-Stokes flows are nearly as intermittent as in three dimensions although no intermittency is present in the inverse cascade in 2D Eulerian turbulence. In addition, an inertial range is very difficult to detect and it is questionable whether it exists at all. Here, we investigate th...

Onsager's point vortex model of two dimensional turbulence is extended by the inclusion of time dependent vortex circulations. If the time dependence of the circulations is governed by statistically independent Onstein-Uhlenbeck processes we observe the emergence of scaling regimes for the structure functions of the Eulerian and the Lagrangian velo...

We report on the detailed experimental determination of the threshold for modulational instability in a photorefractive single-mirror feedback system using a Fourier control technique. Results are compared to analytical predictions and a disagreement for the experimentally significant multiple pattern region is found. Implications for the generatio...

This study investigates spontaneous pattern formation in a single mirror feedback system with a photorefractive nonlinearity. This paper also presents a detailed experimental investigation of the instability threshold for a wide range of system parameters. Fourier filtering techniques are employed experimentally in order to determine the unstable t...