Estimation of drift and diffusion functions from time series data: A maximum likelihood framework

Complex systems are characterized by a huge number of degrees of freedom often interacting in a non-linear manner. In many cases macroscopic states, however, can be characterized by a small number of order parameters that obey stochastic dynamics in time. Recently techniques for the estimation of the corresponding stochastic differential equations from measured data have been introduced. This contribution develops a framework for the estimation of the functions and their respective (Bayesian posterior) confidence regions based on likelihood estimators. In succession approximations are introduced that significantly improve the efficiency of the estimation procedure. While being consistent with standard approaches to the problem this contribution solves important problems concerning the applicability and the accuracy of estimated parameters.Comment: 18 pages, 2 figure

Urolithiasis in a Patient Ingesting Pure Silica: A Scanning Electron Microscopy Study

A patient who repeatedly produced urinary calculi, had consumed about 3g of cristobalite (SiO2) per day for many years. Investigations using scanning electron microscopy revealed minute particles containing silicon in the core of the stone as well as in urine sediment. A mechanism similar to that proposed for the effect of silicon-containing drugs against gastric ulcer, may play a role in this formation of silicon-containing urinary stones

Groundwater seepage landscapes from distant and local sources in experiments and on Mars

© 2014 Author(s). Valleys with theater-shaped heads can form due to the seepage of groundwater and as a result of knickpoint (waterfall) erosion generated by overland flow. This ambiguity in the mechanism of formation hampers the interpretation of such valleys on Mars, particularly since there is limited knowledge of material properties. Moreover, the hydrological implications of a groundwater or surface water origin are important for our understanding of the evolution of surface features on Mars, and a quantification of valley morphologies at the landscape scale may provide diagnostic insights on the formative hydrological conditions. However, flow patterns and the resulting landscapes produced by different sources of groundwater are poorly understood. We aim to improve the understanding of the formation of entire valley landscapes through seepage processes from different groundwater sources that will provide a framework of landscape metrics for the interpretation of such systems. We study groundwater seepage from a distant source of groundwater and from infiltration of local precipitation in a series of sandbox experiments and combine our results with previous experiments and observations of the Martian surface. Key results are that groundwater flow piracy acts on valleys fed by a distant groundwater source and results in a sparsely dissected landscape of many small and a few large valleys. In contrast, valleys fed by a local groundwater source, i.e., nearby infiltration, result in a densely dissected landscape. In addition, valleys fed by a distant groundwater source grow towards that source, while valleys with a local source grow in a broad range of directions and have a strong tendency to bifurcate, particularly on flatter surfaces. We consider these results with respect to two Martian cases: Louros Valles shows properties of seepage by a local source of groundwater and Nirgal Vallis shows evidence of a distant source, which we interpret as groundwater flow from Tharsis

Stochastische Modellierung komplexer Systeme:Von den theoretischen Grundlagen zur Simulation athmosphärischer Windfelder

Ein erster Schwerpunkt dieser Arbeit liegt auf Methoden zur statistischen Analyse der Dynamik komplexer Systeme. Die Grundlage bildet hierbei ein von Friedrich und Peinke entwickeltes Verfahren zum Schätzen von Drift- und Diffusionsfunktionen aus Messdaten. Im Anschluss daran werden Continuous Time Random Walks (CTRWs) betrachtet, die die Modellierung von nicht-Markov-Prozessen, die im Ensemble anomales Diffusionsverhalten zeigen, ermöglichen. Insbesondere wird ein Algorithmus zur numerischen Simulation stetiger Trajektorien solcher Prozesse eingeführt. Auf der Basis von CTRWs wird schließlich ein Modell für atmosphärische Windfelder, die für eine bessere Charakterisierung von Windkraftanlagen in der atmosphärischen Grenzschicht benötigt werden, entwickelt, das einige intermittente Eigenschaften der atmosphärischen Turbulenz reproduzieren kann. Die Arbeit schließt mit ersten Ergebnissen von Tests dieses Windfeldmodells an aktuellen Windkraftanlagen

Searching for optimal variables in real multivariate stochastic data

By implementing a recent technique for the determination of stochastic eigendirections of two coupled stochastic variables, we investigate the evolution of fluctuations of NO2 concentrations at two monitoring stations in the city of Lisbon, Portugal. We analyze the stochastic part of the measurements recorded at the monitoring stations by means of a method where the two concentrations are considered as stochastic variables evolving according to a system of coupled stochastic differential equations. Analysis of their structure allows for transforming the set of measured variables to a set of derived variables, one of them with reduced stochasticity. For the specific case of NO2 concentration measures, the set of derived variables are well approximated by a global rotation of the original set of measured variables. We conclude that the stochastic sources at each station are independent from each other and typically have amplitudes of the order of the deterministic contributions. Such findings show significant limitations when predicting such quantities. Still, we briefly discuss how predictive power can be increased in general in the light of our methods

Wind Energy and the Turbulent Nature of the Atmospheric Boundary Layer

Wind turbines operate in the atmospheric boundary layer, where they are exposed to the turbulent atmospheric flows. As the response time of wind turbine is typically in the range of seconds, they are affected by the small scale intermittent properties of the turbulent wind. Consequently, basic features which are known for small-scale homogeneous isotropic turbulence, and in particular the well-known intermittency problem, have an important impact on the wind energy conversion process. We report on basic research results concerning the small-scale intermittent properties of atmospheric flows and their impact on the wind energy conversion process. The analysis of wind data shows strongly intermittent statistics of wind fluctuations. To achieve numerical modeling a data-driven superposition model is proposed. For the experimental reproduction and adjustment of intermittent flows a so-called active grid setup is presented. Its ability is shown to generate reproducible properties of atmospheric flows on the smaller scales of the laboratory conditions of a wind tunnel. As an application example the response dynamics of different anemometer types are tested. To achieve a proper understanding of the impact of intermittent turbulent inflow properties on wind turbines we present methods of numerical and stochastic modeling, and compare the results to measurement data. As a summarizing result we find that atmospheric turbulence imposes its intermittent features on the complete wind energy conversion process. Intermittent turbulence features are not only present in atmospheric wind, but are also dominant in the loads on the turbine, i.e. rotor torque and thrust, and in the electrical power output signal. We conclude that profound knowledge of turbulent statistics and the application of suitable numerical as well as experimental methods are necessary to grasp these unique features (...)Comment: Accepted by the Journal of Turbulence on May 17, 201