SFB 1294
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    • Research Area A Theory and Algorithms
      • A01 Statistics for stochastic partial differential equations
      • A02 Long-time stability and accuracy of ensemble transform filter algorithms
      • A03 Sequential and adaptive learning under dependence and non-standard objective functions
      • A04 Nonlinear statistical inverse problems with random observations
      • A05 Combining non parametric statistical and probabilistic approaches for inference on cloud-of-points data
      • A06 Approximative Bayesian inference and model selection for stochastic differential equations (SDEs)
      • A07 Model order reduction for Bayesian inference
    • Research Area B Algorithms and Applications
      • B02 Inferring the dynamics underlying protrusion-driven cell motility
      • B03 Parameter inference and model comparison in dynamical cognitive models
      • B04 Parametric and nonparametric modeling of spatiotemporal change patterns in seismicity using Hawkes processes
      • B05 Attention selection and recognition in scene-viewing
      • B06 Novel methods for the 3D reconstruction of the dynamic evolution of the Van Allen belts using multiple satellite measurements
      • B07 Inferring active particle dynamics by data assimilation
      • B08 Continuous learning by integrating reinforcement learning and data assimilation to individualise drug treatments
      • B09 Neural network modelling of brain responses during language comprehension
    • Research Area Z Common Activities
      • Z03 Information Infrastructure for data assimilation
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  1. Data assimilation - the collaborative research centre SFB1294
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Colloquia in 2018

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12Jan2018

Pharmacometrics: Analysing and understanding variability and uncertainty in clinical data

Charlotte Kloft, Freie Universität Berlin, Germany 2.28.0.10810:15-11:45

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02Feb2018

Data Assimilation: Past, Present and Future

Michael Ghil, Ecole Normale Supérieure, Paris, France 2.28.0.10810:15-11:45

We introduce basic ideas and methods of data assimilation in meteorology and oceanography, and illustrate their progress from numerical weather…

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27Apr2018

On periodic signals in stochastic Hodgkin-Huxley models

Reinhard Höpfner, Universität Mainz, Germany 2.28.0.10810:15 - 11:45

We consider a stochastic Hodgkin-Huxley model where dendritic input -modelled as an autonomous SDE which depends on a deterministic T-periodic signal…

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25May2018

Approximate Kernel Embeddings of Distributions

Dino Sejdinovic, University of Oxford, UK 2.28.0.10810:15 - 11:45

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting probability metric, are useful tools for fully nonparametric…

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22Jun2018

Numerical Methods in Visual Computing: what we can learn from each other

Uri Ascher, University of British Columbia (UBC), Vancouver, Canada 2.28.0.10810:15 - 11:45

Visual computing is a wide area that includes computer graphics and image processing, where the “eyeball-norm” rules. I will discuss two case studies…

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13Jul2018

A review of the Epidemic Type Aftershock Sequences (ETAS) model: 30 years of modeling seismicity

Maximilian Werner, University of Bristol, UK 2.28.0.10810:15 - 11:45

Earthquakes seldom come alone. They occur in temporal and spatial clusters, occasionally preceded by foreshocks, always followed by aftershocks, and…

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24Aug2018

1st Kalman-Lecture of the SFB

Andrew Stuart, Caltech (California Institute of Technology), USA 2.27.0.0110:15- 11:45

The Legacy of Rudolph Kalman

In 1960 Rudolph Kalman published what is arguably the first paper to develop a systematic, principled approach to the…

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09Nov2018

Image-based modelling of problems in cell motility

Till Bretschneider, The University of Warwick, UK 2.28.0.10810:15 -11:45

Mathematical modelling has been key to understanding the mechanics of cell shapes and the theoretical principles behind the complex spatio-temporal…

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09Nov2018

Postponed - Equal-weight particle filters for high-dimensional geoscience applications

Peter Jan van Leeuwen, University of Readings, UK 2.28.0.10810:15 - 11:45

This talk has been postponed- we will inform you about the new date in the Events section.

Particle filters hold the promise of fully nonlinear data…

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07Dec2018

Piecewise-deterministic Markov chain Monte Carlo

Arnaud Doucet, Oxford University 2.28.0.10810:15 - 11:45

A novel class of continuous-time non-reversible Markov chain Monte Carlo (MCMC) based on piecewise-deterministic processes has recently emerged. In…

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Current Colloquia
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Institute of Mathematics

SFB 1294 – Data Assimilation
Institute of Mathematics
Karl-Liebknecht-Str. 24-25
14476 Potsdam - OT Golm

SFB1294[at]uni-potsdam.de
+49 331 977 203137

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