algorithm

Unveiling the potential of energy/entropy stable numerical methods for hyperbolic/mixed hyperbolic-parabolic PDEs

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B9 L2 H1

Abstract The demand for increasingly multidisciplinary reliable simulations, for both analysis and design optimization purposes, requires transformational advances in individual components of future PDEs solvers. At the algorithmic level, hardware compatibility and efficiency are of paramount importance in determining viability on future hardware. However, equally important (if not more so) is provable algorithmic robustness which becomes progressively more challenging to achieve as problem size and physics complexity increase. We show that rigorously designed adaptive semi- and fully-discrete

Dominik L. Michels receives the first Procter & Gamble Faculty Award

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Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Robots learn by checking in on team members

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Innovative drone designs and software enables a team of drones to work together in a coordinated approach.

A race against time

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The theme of the University's 2019 Winter Enrichment Program (WEP) was "time." It represented an opportune moment for KAUST to welcome members of the McLaren Group to campus to discuss the recently signed extreme performance research partnership between KAUST and McLaren.

Changing how we predict coral bleaching

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A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.

Teamwork pays off at startup Insyab

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Insyab, a technology startup specializing in smart solutions allowing robots and drones to collaborate on the execution of common tasks, resulted from three years of its founders' dedicated research at KAUST.

An algorithm for success: KAUST-designed differential equation-solving algorithm included in Maple 2020

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A KAUST-designed symbolic algorithm to solve nonlinear ordinary differential equations (ODEs) has been selected for inclusion in the current version of the computer algebra system (CAS) Maple 2020. The global symbolic and numeric computing software system—developed by the Canadian company Waterloo Maple (Maplesoft)—is widely used by scientists, engineers, and researchers to analyze, visualize, and solve mathematical problems.

Xun Qian

Xun Qian is a Postdoctoral Research Fellow at the Visual Computing Center (VCC), King Abdullah University of Science and Technology (KAUST), working with Professor Peter Richtárik in his research group. Education and Early Career Dr. Qian obtained his B.Sc. degree in Mathematics from Huazhong University of Science and Technology, China, in 2013. In 2017, he has obtained a Ph.D. degree in Mathematics from Hong Kong Baptist University (HKBU), Hong Kong. Research Interest Xun Qian is interested in stochastic methods and algorithms, machine learning and interior-point methods. Selected