SAMM 2017

SAMM 2017

The topics of the Summer School on Applied Mathematics and Mechanics (SAMM) are Bayesian inference and uncertainty quantification, data analysis and processing. Bayesian inference is "rippling through everything from physics to cancer research, ecology to psychology," (The New York Times). The main idea of this summer school is to give a more detailed insight into the Bayesian probabilistic way of thinking and numerics behind of that. The lectures will be given on probability theory, uncertainty quantification and data analysis starting from the basics but also emphasizing their application in everday life. Some of topics that will be covered are

Topics:

  • Probability theory
  • Uncertainty quantification

    • Data-driven numerical methods for uncertainty quantification
    • Functional approxiamtion approaches (e.g. Stochastic Galerkin approaches)
    • Coping with high dimensional models and data sets
    • Uncertainty propagation in time depedent nonlinear systems

  • Bayesian inference

    • classical sampling approaches (e.g. straightforward and adaptive Markov chain Monte Carlo)
    • approximate filtering
      • particle filters
      • Kalman like filtering
      • spectral based Kalman filtering
    • Coping with high dimensional posterior and data sets
    • hierarchical filtering

  • Machine and deep learning

    • Who can attend?

    The school is open to international postdoc researchers up to two years after PhD studies, PhD and advanced undergraduate students from engineering sciences or mathematics. Please visit registration page.