Tag: ML
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Diving into MareNostrum 5: CEEC at MNHACK24
This fall, a group of researchers from across Europe gathered at the Barcelona Supercomputing Center (BSC) for MNHACK , the sixth hackathon centered on the MareNostrum systems, this time MareNostrum 5. Among them were many CEEC-ers there to prepare our codes for optimal performance on this young machine while exploring its scalability and energy consumption.
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Programming complex workflows with PyCOMPSs
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Programming large-scale systems poses several challenges to scientific application developers. Join us for a webinar on PyCOMPSs, a pioneering approach to task-based programming in Python that enables codes to be executed in distributed computing platforms. This webinar will give an overview of PyCOMPSs illustrated with examples in development at BSC that include CFD simulations with…
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Call for Abstracts! High-fidelity CFD with application in ship hydrodynamics
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Submit your abstract to our invited session at Marine 2025!! The session titled “High-fidelity CFD with application in ship hydrodynamics” is seeking submissions by December 15th, 2024. Full details and guidelines for submission are available in the proposal .
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FLEXI/GALÆXI: Open-Source Solver for Multiscale Flows
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Join us for the 11th CASTIEL Code of the Month to learn about FLEXI/GALÆXI! Both solvers provide a high-order consistent simulation tool chain for solving the compressible Navier–Stokes equations in a highly efficient, accurate and robust manner in a high performance computing setting either on CPU-based systems (FLEXI) or GPUaccelerated clusters (GALÆXI).
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Introduction to Computational Fluid Dynamics
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Join our Anna Schwarz as one of the instructors for the reoccuring “Introduction to Computational Fluid Dynamics” course organized by HLRS, IAG (University of Stuttgart) and the Institute of Software Methods for Product Virtualisation (DLR).
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Entropy stable subcell shock capturing scheme for high-order discontinuous Galerkin methods on moving meshes
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If you’re not in the mini-symposium with Samual, make sure to see the talk ‘Entropy stable subcell shock capturing scheme for high-order discontinuous Galerkin methods on moving meshes’ presented by Anna Schwarz in room 1.14.
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ECCOMAS MS088 – State-of-the-art Machine Learning Techniques For Computational Fluid Dynamics
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Machine learning (ML) in scientific applications including computational fluid dynamics (CFD) is a growing field of research. However, ML can be less stable and more prone to errors in CFD because of its complexity relative to e.g. game theory. Thus, recent research has concentrated on reinforcement learning (RL) or physics-informed methods applied to CFD. Another…