Florian Arbes: Non-intrusive reduced order modelling of parameterized partial differential equations

Abstract: In computational mechanics, high fidelity simulations of a parameterized partial differential equation (PDE) are often computational expensive, which make them impractical for real-time predictions. Non-intrusive reduced order modelling aims to address this problem with a fast low rank approximation. This is usually done in two phases: the model is built in the offline phase and the prediction is done in the online phase. In the offline phase, data points, or so-called snapshots, are collected from simulations or measurements. The reduced basis space can then be obtained from the dataset using Proper Orthogonal Decomposition. In the online phase, the solution for a new set of parameters is obtained by first recovering the expansion coefficients for the reduced basis and then projecting them back into the uncompressed real-life space. The non-intrusive approach relies on a statistical mapping between the coefficients and the parameters. Various methods have been proposed to do so, this seminar will discuss radial basis function interpolations and dynamic mode decompositions.

This talk is part of the Mechanics Lunch Seminar series. That means 20min talks plus discussion in an informal setting.

Zoom: To obtain the Zoom meeting details please contact Timo Koch (timokoch at math.uio.no).

Published May 18, 2021 12:19 PM - Last modified May 18, 2021 12:19 PM