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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
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
25:36 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

MRI data models at low SNR

In this talk we elaborate the effect of low SNR on estimated parameters in models for neuroimaging data. We will present a new method for the local estimation of the noise parameter in the signal distribution and demonstrate how this can be used for improved estimation of model parameters. (joint work with Joerg Polzehl).
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
14:53 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

Estimation of cardiovascular system parameter from real data

This talks shows preliminary results for the estimation of some parameters regarding the cardiovascular system (e.g., vessel mechanical properties, vasculature resistance) using real patient data. The mathematical model is based on efficient one-dimensional network for the blood flow simulation and on the unscented Kalman filtering for the parameter estimation.
  • 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
13:52 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

An online coupled Lagrangian particle dispersion model for COSMO

Lagrangian particle dispersion models (LPDM) are a well-known method for modeling exhaust gas distributions and similar problems. Open accessible LPDM's, e.g., the FLEXPART model are designed for meso-scale simulations and work offline coupled with the coarse frequented output data of any numerical weather prediction model. But the central issue there is that high resolution simulations of small scale phenomena need a high frequency input of meteorological data fields to work accuracy, which can directly be provided by an online coupled model system. Based on the COSMO trajectory module the model LAPASI was developed that integrates an online coupled Lagrangian particle transport into the default COSMO version. It supports any kind of simulation that are possible with the COSMO including idealized cases and can handle a couple Million particles with individual start times, start locations and dry depositions velocities. Thus, LAPASI is a useful extension for COSMO and a necessary addition to the previously existing LPDM's.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
18:34 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Spatial Segmentation Algorithm for Stratified Random Sample considering geometric circumstances

We developed a Segmentation Algorithm, which is based on Region Growing, for Stratified Random Sample. But instead of raster data our algorithm is using vector data! By the use of accurate vector data we can easily implement barriers, like rivers or railways, to control the growing we need to. Also by splitting polygons we achieve a kind of density gradient to slow the grow in unwanted regions, like bridges. As well we had to remove a lot of bottlenecks to reduce runtime and make it performant and practicable. The result are approximately optimal segmented regions, which are met certain conditions regarding the sample design and take consideration to geometric circumstances. In our case it is useful for reduction of costs on Germany-wide field survey for random sample of non-residential building, but is also applicable to any other wanted segmentation.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
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AV-Portal 3.8.0 (dec2fe8b0ce2e718d55d6f23ab68f0b2424a1f3f)