Campuses:

Model Reduction

Thursday, September 7, 2017 - 1:15pm - 1:50pm
Ville Kolehmainen (University of Eastern Finland)
The approximation error approach was proposed in [J. Kaipio \& E. Somersalo, Statistical and Computational Inverse Problems, Springer, 2004] for handling modelling errors due to model reduction and unknown nuisance parameters in inverse problems. In this talk, we discuss the application of the approximation error approach for approximate marginalization of modelling errors caused by inaccurately known sensor parameters in diffuse optical tomography.
Tuesday, April 25, 2017 - 9:00am - 10:00am
Chao Yang (Lawrence Berkeley National Laboratory)
In the time-dependent density functional theory framework, the optical absorption spectrum of a molecular system can be estimated from the trace of the dynamic polarizability associated with the linear response of the charge density to an external potential perturbation of the ground state Hamiltonian.
Wednesday, March 16, 2016 - 10:30am - 11:00am
John Singler (Missouri University of Science and Technology)
Balanced POD is a data-based model reduction algorithm that has been widely used for linearized fluid flows and other linear PDE systems with inputs and outputs. We discuss recent work on balanced POD for such systems, including the case of unbounded input and output operators as can occur when control actuators and sensors are located on the boundary of the physical domain. We also discuss challenges for model reduction of linear and nonlinear PDE systems.
Wednesday, October 22, 2014 - 10:55am - 11:40am
Joachim Schöberl (Technische Universität Wien)
Many problems involve small parameters such that the limit problem is a practically good approximation. As examples we consider the thickness of a thin elastic structure, and the Knudson number (mean free path) in the Boltzmann Equation. The traditional way is to derive first a hierarchy of reduced models, which are then discretized. Our aim is to discretize directly the original equation. Typically, one of the discretization parameters can be reinterpreted as choice of the model.
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