Aktivitäten

27.01.2020, 15:00  –  Campus Golm, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Haus 14, Raum 2.14.0.47
Berlin-Brandenburgisches Seminar Mathematik und ihre Didaktik

Embodied Movement in Mathematics Education

Prof. Arthur Bakker (Freudenthal-Institut, Niederlande)

Can moving the body have added value in learning mathematics? Much research on embodied cognition suggests that cognition is distributed across brain, body, tools, environment, and culture. Far less... 

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27.01.2020, 16:15  –  Campus Golm, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Haus 14, Raum 2.14.0.47
Berlin-Brandenburgisches Seminar Mathematik und ihre Didaktik

Measurement as relational, intensive and inclusive

Prof. Nathalie Sinclair (Simon-Fraser-University, Canada)

To measure is at once a practical and a conceptual engagement with the world. At its most basic, measuring involves empirical encounters (broadly conceived) that allow one to assign various... 

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31.01.2020, 10:15-11:15  –  Campus Golm, Haus 26, Raum 0.76
SFB-Seminar

Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

Marc Bocquet (École des Ponts ParisTech)

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from observations, resorting in particular to residual neural networks. These... 

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03.02.2020, 14:15-15:15  –  TU Berlin Mathematikgebäude Raum MA748
SFB-Seminar

Implicit equation-free methods applied on noisy slow-fast systems

Anna Dittus, Universität Rostock

Slow-fast systems consist of slow macroscopic and fast microscopic dynamics. By using equation-free methods, one can do a complete bifurcation analysis of these slow macroscopic variables, even if... 

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14.02.2020, tba  –  tba
SFB-Seminar

Statistics for chaotic dynamics and random patterns

Heikki Haario, LUT University (Technische Universität Lappeenranta), Finland

We discuss methods for creating Gaussian likelihoods for data that does not directly follow any known statistics. Obvious summary statistics are available, such as averages, but they turn out to lose... 

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14.02.2020, tba  –  tba
SFB-Seminar

Posterior Inference for Sparse Hierarchical Non-stationary Models

Lassi Roininen, University of Oulu, Finland

Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such... 

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25.02.2020, tba  –  tba
SFB-Seminar

Contaminant dispersal, numerical simulation, and stochastic PDEs

Tony Shardlow (University of Bath)

Atmospheric dispersal of contaminants such as ash can be modelled by stochastic differential equations coupled to a large-scale weather model. We develop this model as used by the UK Met Office and... 

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