Eldorado - Repositorium der TU Dortmund

Ressourcen aus und für Forschung, Lehre und Studium

Bei diesem Service handelt es sich um das Institutionelle Repositorium der Technischen Universität Dortmund. Hier werden Ressourcen aus und für Lehre, Studium und Forschung gespeichert, erschlossen und der Öffentlichkeit zugänglich gemacht.

Hauptbereiche in Eldorado

Wählen Sie einen Bereich, um dessen Inhalt anzusehen.

Gerade angezeigt 1 - 5 von 9

Fach- und Universitätsübergreifendes 1319
Fakultäten 13800
Graduate School und Graduiertenkollegs 12
Hochschulverwaltung 1246
Sonderforschungsbereiche 1593

Aktuellste Veröffentlichungen

Edge-modes at interfaces between periodic media via reduced spatial dynamics near Dirac points
(2026-04-27) Schweizer, Ben
We consider the Helmholtz operator in a d-dimensional waveguide, unbounded in x1-direction. The unperturbed waveguide has periodic coefficients in x1, the perturbations are different for x1 < 0 and x1 > 0. The perturbations are such that a band gap opens from a Dirac point. We show that an interface mode appears, corresponding to an eigenvalue in the band gap. Our proof uses the concept of reduced spatial dynamics and homogenization techniques. It is based on the analysis of the inhomogeneous problem for a right hand side that is a modulated eigenfunction of the unperturbed problem. We construct sequences of approximate solutions by solving ordinary differential equation; as these sequences are unbounded, we can conclude the existence of an eigenvalue.
A Parallel-in-Time Navier-Stokes Solver Using Augmented Lagrangian Acceleration and Space-Time Multigrid Methods
(2026) Dünnebacke, Jonas; Lohmann, Christoph; Turek, Stefan
In this work we present a parallel-in-time solver of the Navier-Stokes equation for low Reynolds numbers. The solver uses an all-at-once Newton-Krylov method and the combination of Augmented Lagrangian and LSC preconditioning leading to embarrassingly parallel pressure solves. For the solution of the unsteady velocity system we compare multigrid waveform relaxation and space-time multigrid. For low and moderate Reynolds numbers this solver can improve the strong scalability compared to a time stepping solver. This method is also applied to a Carreau fluid showing the possible application in the area of transient simulation of polymer melts.
On the Automation and Scaling of a Fictitious-Boundary Method for Non-Newtonian Extrusion Die Flows
(2026-05) Esser, Maximilian; Turek, Stefan
We present a fast, robust and scalable solution approach for implicitly captured domains in the context of extrusion dies for highly viscous fluids, applicable for automated simulation pipelines for industrial relevant 3D problems. We also show the applicability and the challenges for the transition to multi-node GPU systems.
Fast Factor Extraction for Mixed Type Financial Data
(2026) Schmidt, Fabian; Demetrescu, Matei
Empirical research has access to ever larger datasets of mixed data types. The sheer amount of available data may often lead to the use of techniques such as dimensionality reduction or shrinkage. To reduce dimensionality, it is not uncommon to assume that the data are driven by a small number of common factors. For metric, or continuous, data, factor extraction may be conducted by means of standard principal component analysis [PCA]. PCA is, however, not directly applicable to count or binary data. Mixed data types may be dealt with via generalized linear models driven analogously by latent factors, and we discuss fast implementations of maximum likelihood estimators based on specific alternating least squares regressions. We demonstrate their practical applicability in simulations and in an application to factor-based forecasts of excess returns.
Real-Time Monitoring for Stock Return Predictability in Nonstationary Volatility Environments
(2025) Demetrescu, Matei; Schmidt, Fabian; Taylor, A.M. Robert
Stock return predictability, should it exist, is likely to be episodic in nature. In order to exploit such pockets of predictability it is essential that they are rapidly detected, in real-time, as the nascent predictive regime emerges. This will typically entail the repeated (sequential) application of one-shot end-of-sample predictability statistics, updated as new data become available. Consequently, in addition to dealing with the usual inference problems caused by unknown regressor persistence and predictive regression endogeneity, one must also account for the multiple testing issues inherent in such monitoring procedures. In addition, stock returns and/or the predictors commonly used typically exhibit time-varying volatility and it is known that ignoring such data features can result in the spurious detection of predictability. We propose real-time monitoring procedures which take account of these issues. Our preferred monitoring strategy uses a CUSUM-type procedure based on a specific moment condition related to the predictive power of the putative predictor. We implement nonparametric adjustment methods to allow for the possibility of time varying volatility which do not require the practitioner to assume any particular parametric model for volatility. Monte Carlo simulations confirm that our proposed detection procedures display well controlled false positive rate across a range of feasible volatility paths coupled with good power to rapidly detect an emerging predictive episode. The empirical relevance of our proposed monitoring strategy is illustrated in a pseudo real-time monitoring exercise using a well-known dataset of S&P 500 returns.