The Scientific Computing seminar series takes place on Fridays at 13:00. Join on zoom
Upcoming and past events #
- Thursday, Jul 15, 2021 09:30
Georg Hager, Performance counter analysis with Likwid and single node performance assessment
- Friday, Jun 25, 2021 13:00
Thomas Weinhart, University of Twente, TBA
- Friday, Jun 18, 2021 13:00
Alexander Moskovsky, Moscow State University, RSC Group, TBA
- Friday, Jun 4, 2021 13:00
Benjamin Uekermann, University of Stuttgart, TBA
- Friday, May 14, 2021 13:00
Nicole Aretz, RWTH Aachen , Title: Sensor selection for linear Bayesian inverse problems with variable model configurations
Abstract: In numerical simulations, mathematical models such as partial differential equations are widely used to predict the behavior of a physical system. The uncertainty in the prediction caused by unknown parameters can be decreased by incorporating measurement data: by means of Bayesian inversion a posterior probability distribution can be obtained that updates prior information on the uncertain parameters. As experimental data can be expensive, sensor positions need to be chosen carefully to obtain informative data despite a limited budget.
In this talk we consider a group of forward models which are characterized through different configurations of the physical system. The configuration is a non-linear influence on the solution, e.g. the geometry or material of an individual work piece in a production chain. Our goal is to choose one set of sensors for the estimation of an uncertain linear influence whose measurement data is informative for all possible configurations. To this end, we identify an observability coefficient that links the experimental design to the covariance of the posterior. We then present a sequential sensor selection algorithm that improves the observability coefficient uniformly for all configurations. Computational feasibility is achieved through model order reduction. In particular, we discuss opportunities and challenges to decrease the computational cost of the inverse problem via the reduced basis method. We demonstrate our results on steady-state heat conduction problems for a thermal block and a geothermal model of the Perth Basin in Western Australia.
- Thursday, Apr 8, 2021 13:00
Jochim Protze, Title: Asynchronous MPI communication with OpenMP tasks
Abstract: Your communication depends on computation results as input? Your computation task depends on data to arrive from a different process? OpenMP task dependencies should allow to express such dependencies. OpenMP 5.0 introduced detached tasks. In combination with MPI detached communication , this allows to build task dependency graphs across MPI processes. In this short presentation you will learn how you can integrate MPI detached communication into your project and profit from real asynchronous communication. If you don’t want to use OpenMP tasks, the same approach will also work with C++ futures/promises.
Zoom link for this session: https://durhamuniversity.zoom.us/j/97425330730?pwd=Ti92aXRKSXRmN2FPZmNTazdoVEl0QT09
- Friday, Mar 12, 2021 13:00
Tim Dodwell, Alan Turing Institute, University of Exeter, Title: Adaptive Multilevel Delayed Acceptance
Abstract: Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing complexity and cost, and correcting the inexpensive models on-the-fly. The method has been integrated within the open-source probabilistic programming package PyMC3 and is available in the latest development version
- Friday, Feb 5, 2021 13:00
Andy Davis, Courant Institute, Title: Super-parameterized numerical methods for the Boltzmann equation modeling Arctic sea ice dynamics
Abstract: We devise a super-parameterized sea ice model that captures dynamics at multiple spatial and temporal scales. Arctic sea ice contains many ice floes—chunks of ice—whose macro-scale behavior is driven by oceanic/atmospheric currents and floe-floe interaction. There is no characteristic floe size and, therefore, accurately modeling sea ice dynamics requires a multi-scale approach. Our two-tiered model couples basin-scale conservation equations with small-scale particle methods. Unlike many other sea ice models, we do not average quantities of interest (e.g., mass/momentum) over a representative volume element. Instead, we explicitly model small-scale dynamics using the Boltzmann equation, which evolves a probability distribution over position and velocity. In practice, existing numerical methods approximating the Boltzmann equation are computationally intractable when modeling Arctic basin scale dynamics. Our approach decomposes the density function into a mass density that models how ice is distributed in the spatial domain and a velocity density that models the small-scale variation in velocity at a given location. The mass density and macro-scale expected velocity evolve according to a hyperbolic conservation equation. However, the flux term depends on expectations with respect to the velocity density at each spatial point. We, therefore, use particle methods to simulate the conditional density at key locations. We make each particle method independent using a local change of variables that defines micro-scale coordinates. We model small-scale ice dynamics (e.g., collision) in this transformed domain.