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- ItemSpecial issue on conceptual structures(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Alam, Mehwish; Braun, Tanya; Endres, Dominik; Yun, Bruno[no abstract available]
- ItemMaximal Regularity for Non-autonomous Equations with Measurable Dependence on Time(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2016) Gallarati, Chiara; Veraar, MarkIn this paper we study maximal L p-regularity for evolution equations with time-dependent operators A. We merely assume a measurable dependence on time. In the first part of the paper we present a new sufficient condition for the L p-boundedness of a class of vector-valued singular integrals which does not rely on Hörmander conditions in the time variable. This is then used to develop an abstract operator-theoretic approach to maximal regularity. The results are applied to the case of m-th order elliptic operators A with time and space-dependent coefficients. Here the highest order coefficients are assumed to be measurable in time and continuous in the space variables. This results in an L p(L q)-theory for such equations for p,q∈(1,∞). In the final section we extend a well-posedness result for quasilinear equations to the time-dependent setting. Here we give an example of a nonlinear parabolic PDE to which the result can be applied.
- ItemUncertainty Quantification in Image Segmentation Using the Ambrosio–Tortorelli Approximation of the Mumford–Shah Energy(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Hintermüller, Michael; Stengl, Steven-Marian; Surowiec, Thomas M.The quantification of uncertainties in image segmentation based on the Mumford–Shah model is studied. The aim is to address the error propagation of noise and other error types in the original image to the restoration result and especially the reconstructed edges (sharp image contrasts). Analytically, we rely on the Ambrosio–Tortorelli approximation and discuss the existence of measurable selections of its solutions as well as sampling-based methods and the limitations of other popular methods. Numerical examples illustrate the theoretical findings.
- ItemCausal coupling inference from multivariate time series based on ordinal partition transition networks(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Subramaniyam, Narayan Puthanmadam; Donner, Reik V.; Caron, Davide; Panuccio, Gabriella; Hyttinen, JariIdentifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.
- ItemDetection of dynamical regime transitions with lacunarity as a multiscale recurrence quantification measure(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021) Braun, Tobias; Unni, Vishnu R.; Sujith, R.I.; Kurths, Juergen; Marwan, NorbertWe propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot-based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogeneity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors.
- ItemOn the Stokes-Type Resolvent Problem Associated with Time-Periodic Flow Around a Rotating Obstacle(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Eiter, ThomasConsider the resolvent problem associated with the linearized viscous flow around a rotating body. Within a setting of classical Sobolev spaces, this problem is not well posed on the whole imaginary axis. Therefore, a framework of homogeneous Sobolev spaces is introduced where existence of a unique solution can be guaranteed for every purely imaginary resolvent parameter. For this purpose, the problem is reduced to an auxiliary problem, which is studied by means of Fourier analytic tools in a group setting. In the end, uniform resolvent estimates can be derived, which lead to the existence of solutions to the associated time-periodic linear problem.
- ItemQuantitative Heat-Kernel Estimates for Diffusions with Distributional Drift(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Perkowski, Nicolas; van Zuijlen, Willem[For Abstract, see PDF]