Robustness

Reynolds-robust preconditioners for the stationary incompressible Navier--Stokes equations

Prof.Patrick Farrell, University of Oxford

Dec 5, 12:00 - 13:00

B9 L2 R2322 H1

Navier-Stokes equation Robustness

Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.

Assessing DNS Resilience and Robustness

Prof. Fatemah Alharbi, Assistant Professor, the Computer Science Department, Taibah University, Yanbu, KSA.

May 25, 15:30 - 16:30

B4 L5 R5220.

resilience Robustness

The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.

Yulian Wu

Ph.D. Student, Computer Science

machine learning privacy Trustworthy AI decision making decision sciences privacy-preserving AI trustworthy machine learning Robustness Reinforcement Learning quantum machine learning bioinformatics direct preference optimization

Yulian Wu is a Ph.D. candidate under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST).