MS-Mo-D-20
Low-rank Tensor Approximation in Multi-parametric and Stochastic PDEs - Part I of II
For Part II, see MS-Mo-E-20

Date: August 10
Time: 13:30--15:30
Room: 210B

(Note: Click title to show the abstract.)

Organizer:
Litvinenko, Alexander (KAUST, UQ & ECRC Centers)
Matthies, Hermann (TU Braunschweig, Inst. of Scientific Computing)
Nouy, Anthony (Ecole Centrale Nantes)

Abstract: Approximations of stochastic and multi-parametric differential equations may lead to extremely high dimensional problems that suffer from the so called curse of dimensionality. Computational tractability may be recovered by relying on
adaptive low-rank/sparse approximation. The tasks are 1) to keep a low-rank approximation of the high-dimensional input data through the whole computing process, 2) compute the solution and perform a post-processing in a low-rank tensor format. The post-processing may include computation of different statistics, visualization of a small portion of large data, large data analysis. The aim is to develop numerical methods which will reduce the computational cost as well as the storage requirement from O(n^d) to O(knd), where k is a small integer (related with the rank).
The purpose of this minisymposium is to bring together experts in adaptive
discretization/solution of stochastic/multi-parametric problems, experts in
multi-linear algebra and experts in uncertainty quantification methods.


MS-Mo-D-20-1
13:30--14:00
Time-dependent low-rank approximation method for solving parametric dynamical systems
BILLAUD-FRIESS, Marie (Ecole Centrale de Nantes)
Nouy, Anthony (Ecole Centrale Nantes)


MS-Mo-D-20-2
14:00--14:30
Approximating Stochastic Galerkin Operator in the Tensor Train data format
Litvinenko, Alexander (KAUST, UQ & ECRC Centers)
Matthies, Hermann (TU Braunschweig, Inst. of Scientific Computing)


MS-Mo-D-20-3
14:30--15:00
Kolmogorov widths and low-rank approximations of parametric elliptic PDEs
Bachmayr, Markus (UPMC Paris 06)


MS-Mo-D-20-4
15:00--15:30
Adaptive-fiber tensor-trains with application to Bayesian inference
Gorodetsky, Alex (Massachusetts Inst. of Tech.)
Marzouk, Youssef (Massachusetts Inst. of Tech.)

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Footnote:
Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA