Approximation of a marine ecosystem model by artificial neural networks. ETNA - Electronic Transactions on Numerical Analysis

Marine ecosystem models are important to identify the processes that affect for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximate an exemplary marine ecosystem model by different artificial neural networks (ANNs). We use a fully connected network (FCN), then apply the sparse evolutionary training (SET) procedure, and finally apply a genetic algorithm (GA) to optimize, inter alia, the network topology. With all three approaches, a direct approximation of the steady annual cycle is not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results are in very good agreement. In this way, we achieve a runtime reduction by about 15%. The results from the SET algorithm are comparable to those of the FCN. Further application of the GA may lead to an even higher reduction

Mayr-Peyrimsky (geb. Peyrimsky),Anna

* 17.4.1855 Piacenza/I, † 28.10.1916 Graz. Sängerin, Gesangspädagogin

Hrabanek (Hrabaneck), Familie

Prokop: * 2.7.1783 Diwischau/Böhmen (Divišov/CZ), † zw. 1831/42 (Ort?). Musiker

A machine learning framework for LES closure terms. ETNA - Electronic Transactions on Numerical Analysis

In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive a consistent framework for LES closure models, with special emphasis laid upon the incorporation of implicit discretization-based filters and numerical approximation errors. We investigate implicit filter types that are inspired by the solution representation of discontinuous Galerkin and finite volume schemes and mimic the behavior of the discretization operator, and a global Fourier cutoff filter as a representative of a typical explicit LES filter. Within the perfect LES framework, we compute the exact closure terms for the different LES filter functions from direct numerical simulation results of decaying homogeneous isotropic turbulence. Multiple ANN with a multilayer perceptron (MLP) or a gated recurrent unit (GRU) architecture are trained to predict the computed closure terms solely from coarse-scale input data. For the given application, the GRU architecture clearly outperforms the MLP networks in terms of accuracy, whilst reaching up to 99.9% correlation between the networks' predictions and the exact closure terms for all considered filter functions. The GRU networks are also shown to generalize well across different LES filters and resolutions. The present study can thus be seen as a starting point for the investigation of data-based modeling approaches for LES, which not only include the physical closure terms, but account for the discretization effects in implicitly filtered LES as well

Meyer, Robert

(1855 - 1914), Verwaltungsbeamte

On multidimensional sinc-Gauss sampling formulas for analytic functions. ETNA - Electronic Transactions on Numerical Analysis

Using complex analysis, we present new error estimates for multidimensional sinc-Gauss sampling formulas for multivariate analytic functions and their partial derivatives, which are valid for wide classes of functions. The first class consists of all n-variate entire functions of exponential type satisfying a decay condition, while the second is the class of n-variate analytic functions defined on a multidimensional horizontal strip. We show that the approximation error decays exponentially with respect to the localization parameter N. This work extends former results of the first author and J. Prestin, [IMA J. Numer. Anal., 36 (2016), pp. 851–871] and [Numer. Algorithms, 86 (2021), pp. 1421–1441], on two-dimensional sinc-Gauss sampling formulas to the general multidimensional case. Some numerical experiments are presented to confirm the theoretical analysis

A non-intrusive method to inferring linear port-Hamiltonian realizations using time-domain data. ETNA - Electronic Transactions on Numerical Analysis

Port-Hamiltonian systems have gained a lot of attention in recent years due to their inherent valuable properties in modeling and control. In this paper, we are interested in constructing linear port-Hamiltonian systems from time-domain input-output data. We discuss a non-intrusive methodology that is comprised of two main ingredients–(a) inferring frequency response data from time-domain data and (b) constructing an underlying port-Hamiltonian realization using the inferred frequency response data. We illustrate the proposed methodology by means of two numerical examples and also compare it with two other system identification methods to infer the frequency response from the input-output data

Epistemische Sicherheit. Zur Rolle wissenschaftlicher Expertise in chronischen Krisen (EPISTEMIS)

Das Projekt EPISTEMIS will auf Basis empirischer Forschung herausarbeiten, welche Herausforderungen sich in einer chronischen Krise wie der Covid-19-Pandemie für institutionelle Politikberatung ergeben. Grundlage dafür ist ein internationaler Vergleich (Österreich, Deutschland, Großbritannien). Das Projekt will auf Basis dieser Analyse praktische Hinweise liefern, auf welche Weise wissenschaftliche Expertise organisiert sein sollte, um den komplexen Anforderungen einer solchen globalen Krise gerecht zu werden

Maier, Robert von; Ps. Lenor

(1855 - 1900), Schauspiele