11.09.16 - 16.09.16, Seminar 16372

Uncertainty Quantification and High Performance Computing

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

High performance computing (HPC) is a key technology to solve large-scale real-world simulation problems on parallel computers. Simulations for a fixed, deterministic set of parameters are current state of the art. However, there is a growing demand in methods to appropriately cope with uncertainties in those input parameters. This is addressed in the developing research field of uncertainty quantification. Here, (pure) Monte-Carlo methods are easy to parallelize and thus fit well for parallel computing. However, their weak approximation capabilities lead to slow convergence.

The proposed seminar aims at bringing together experts in the fields of uncertainty quantification and high performance computing. Contributions with an industrial background are strongly encouraged. Discussions on the latest numerical techniques beyond pure Monte-Carlo such as Polynomial Chaos, Stochastic Collocation, Gaussian Process Regression, Quasi Monte-Carlo or Multi-Level Monte Carlo shall be fostered. The covered topics will include, but are not limited to inference, control and optimization under uncertainties on HPC systems, scalable multi-level, higher-order and low discrepancy methods, parallel adaptive methods, model reduction, parallelization techniques, parallel software frameworks and resilience. These topics shall be put in context of large-scale real-world problems on parallel computers.