Navigation

DE/EN

Kolloquium des Departments Mathematik

Jun 11
11-06-2019 16:30 Uhr bis 17:30 Uhr
HS H13

Low-rank tensor methods for uncertain PDE-constrained optimization

Referent: Prof. Dr. Peter Benner, MPI Magdeburg
Veranstalter: Eberhard Baensch

Abstract:
We discuss optimization and control of unsteady partial differential equations (PDEs), where some coefficients of the PDE as well as the control may be uncertain. This may be due to a lack of knowledge about the exact physical parameters, like material properties describing a real-world problem („epistemic uncertainty“), or the inability to apply a computed optimal control exactly in practice. Using a stochastic Galerkin space-time discretization of the optimality system resulting from such PDE-constrained optimization problems under uncertainty leads to large-scale linear or nonlinear systems of equations in saddle point form. Nonlinearity is treated with a Picard-type iteration in which linear saddle point systems have to be solved in each iteration step. Using data compression based on separation of variables and the tensor train (TT) format, we show how these large-scale indefinite and (non)symmetric systems, typically having $10^8$ to $10^{15}$ unknowns, can be solved without the use of HPC technology. The key observation is that the unknown and the data can be well approximated in a new block TT format that reduces complexity by several orders of magnitude. As examples, we consider control and optimization problems for the linear heat equation, the unsteady Stokes and Stokes-Brinkman equations, as well as the incompressible unsteady Navier-Stokes equations. The talk reviews the results published in [1, 2] and provides new results for the Navier-Stokes case.