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47:23 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

"Extreme Fluids" - Some Examples, Challenges and Simulation Techniques for Flow Problems with Complex Rheology

In this talk we discuss numerical simulation techniques for incompressible fluids with complex rheology which means that local flow characteristics may differ significantly by several orders of magnitude, for instance due to non-isothermal behavior and pressure, resp., shear dependent viscosity. Such fluids usually include viscoplastic as well as viscoelastic effects which is typical for yield-stress fluids, granular material as well as polymer melts and kautschuk. Corresponding applications are relevant for polymer processing, but include also viscoplastic lubrication, fracking and macro encapsulation. In this talk, we present special discretization and solver techniques in which case the coupling between the velocity, pressure and additional variables for the stresses, which leads to restrictions for the choice of the FEM approximation spaces, and the (often) hyperbolic nature of the problem are handled with special Finite Element techniques including stabilization methods. The resulting linearized systems inside of outer Newton-like solvers are (special) nonsymmetric saddle point problems which are solved via geometrical multigrid approaches. We illustrate and analyze numerically the presented methodology for well-known benchmark configurations as well as protoypical industrial applications for several nonlinear flow models.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
52:42 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

Machine learning and applications

Since a few years Machine Learning (ML) has broadened the modeling toolbox for the sciences and industry. The talk will first remind the audience of the main ingredients for applying machine learning. Then various ML applications in the sciences namely Brain Computer Interfaces and Quantum Chemistry will be discussed.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
25:03 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Mathematical knowledge management as a route to sustainability in mathematical modeling and simulation

Mathematical modeling and simulation (MMS) has now been established as an essential part of the scientific work in many disciplines. It is common to categorize the involved numerical data and to some extend the corresponding scientific software as research data. Both have their origin in mathematical models. A holistic approach to research data in MMS should cover all three aspects: models, software, and data. Yet it is unclear, whether a suitable management of the mathematical knowledge related to models is possible and how it would look like. In this talk, we outline an approach to address this problem based on a flexiformal representation of the mathematical knowledge in scientific publications and discuss how this can contribute to sustainable research in MMS.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
42:16 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

An introduction to inverse problems with applications in machine learning

The presentation starts with some motivating examples of Inverse Problems before introducing the general setting. We shortly review the most common regularization approaches (Tikhonov, iteration methods) and sketch some recent developments in sparsity and machine learning. Sparsity refers to additional expert information on the desired reconstruction, namely, that is has a finite expension in some predefined basis or frame. In machine learning we focus on 'multi colored' inverse problems, where part of the application can be formulated by a strict analytical framework but some part of the problem needs to modeled by a data driven approach. Those combined problems can be created by data- driven linear low rank approximations or more general black box models. In particular we review deep learning approaches to inverse problems. Finally, machine learning techniques by themselves are often inverse problems. We highlight basis learning techniques and applications to hyperspectral image analysis.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
44:53 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

Welcome/Opening MMS Days

  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
25:37 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Thermodynamically consistent modeling of fluids

The motion of fluids is restricted by the 2nd Law of Thermodynamics in multiple manner. This lecture uses historical and contemporary issues to illustrate both the general structure and special properties of thermodynamically consistent modeling.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
24:27 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

On perspectives for common research in the Leibniz MMS network in the field of Computational and Geophysical Fluid Dynamics

Computational Fluid Dynamics (CFD) and Geophysical Fluid Dynamcis (GFD) are common research topics of different Leibniz institutes, where very similar mathematical and physical modelling approaches are used. Therefore, CFD & GFD seem to be obvious candidates for interdisciplinary research in the Leibniz Association. In the talk, an overview is given about the first common MMS research activities in the field of (CFD & GFD), and some perspectives for common research are presented.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
14:53 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Investigation of phenomena in the Western Baltic Sea

To understand the processes of local phenomena over the Baltic Sea such as Coastal Upwelling or Salinity Inversion, we are coupling an atmosphere and ocean model with the Earth System Modelling Framework (ESMF). For the atmospheric part the operational model of the German Weather Service (ICON) is utilized in a nested limited area mode. The General Estuarine Turbulence Model (GETM) has been chosen for the local ocean model. Typical coupling issues are the different grid schemes of the models and hence, a set and choice of suitable interpolation/regridding methods is required. Within our framework, the state variables (e.g. temperature) and flux data (e.g. heat flux) has to be interpolated from the unstructured triangular grid of ICON to the structured rectangular latitude longitude grid of GETM and vice versa. Furthermore, due to different grids, the land sea masking of each model has to be considered for the interpolation. Additionally, when using a parallel infrastructure, the number of processes has to be chosen such that the coupled model runs well balanced. Since we are using the concurrent structure ESMF is providing, the focus is on the reduce of possible waiting time for each model. The presentation shall give an overview about these issues, how we are addressing them within our coupled model framework and some results of first runs.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
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