Machine learning and data-driven methods for model reduction and control
A 5 days workshop on *Machine Learning in Model Reduction and Control* that will be held in Shanghai, China, on October 14--18 (2019). There will be 40 participants including 15 from Germany and maybe a few from other countries.
Aug 3, 2019
Tensor-space Galerkin POD for parametric flow equations
Jul 18, 2019
Robust observer-based feedback for the incompressible Navier-Stokes equation
Jul 17, 2019
Robust control for compensation of linearization and discretization errors in stabilization of incompressible flows
Jul 3, 2019
MS -- Low-rank modelling in uncertainty quantification
Mini Symposium Data based methods have been in use for model order reduction (MOR) or control systems design (CSD) for several decades. Classical system identification approaches and established model reduction techniques like Proper Orthogonal Decomposition all follow the idea of using data to fit or enhance a model.
Jan 1, 0001
Low-rank modelling in uncertainty quantification
Mini Symposium Uncertainties in modelling and simulation of dynamical systems come from different sources: modelled or unmodelled external perturbations, unknown or stochastically changing model parameters, and measurement errors, to name a few.
Jan 1, 0001