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MSDI1: Statistical inference of multiscale systems / Adaptive sampling and optimisation of multiscale energy landscapes

Mar 02, 2021 | 11:00 AM - 04:30 PM

Chairs: Nikolas Nüsken, Sebastian Reich, Carsten Hartman, Nikki Vercauteren

Filtering, parameter estimation, data assimilation or smoothing of time series are inverse problems that can be rephrased as optimisation tasks. For multiscale systems, the oscillatory nature of the underlying forward problem creates additional difficulties, in addition to the general difficulty of solving inverse problems that are typically ill-posed. A common approach to deal with multiscale effects is to replace the original dynamics with a reduced model that is used to solve an inverse problem.

The mission of this minisymposium is twofold: (1) to discuss when such an approach is mathematically appropriate and algorithmically functional, (2) to discuss the application of surrogate modelling approaches based on novel optimisation and machine learning algorithms. It features contributions from both theorists and practitioners from various fields of science, mathematics and statistics.

Find abstracts for all talks here or linked individually below.


Georg Gottwald (University of Sydney)

Supervised learning from noisy observations: Combining machine-learning techniqueswith data assimilation


Vyacheslav Boyko (FU Berlin)

Application of a model-based clustering method to parameterize a non-linear and non-stationary process in the atmospheric boundary layer


Franca Hoffmann (Hausdorff Center, U Bonn)

Consensus based sampling

12:30 Open Discussion


Lunch Break


Michela Ottobre (Heriot Watt University, Edinburgh)

Uniform in time approximations of stochastic dynamics


Justin Sirignano (U Oxford)

Deep Learning PDE Models: Convergence Analysis and Applications




Open Discussion

Continuation on Wednesday


Michele Pavon (U Padova)

Fast cooling for network flows


Marie-Therese Wolfram (U Warwick)

Inverse optimal transport