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Markus Geveler

Diplominformatiker

Name	Markus Geveler
Ort	Deutschland
Beruf	Diplominformatiker (charakteristischer Beruf)
Variante	M. Geveler
Datensatztyp	Person
GND-Nummer	140948910
Datensatz-ID	140948910
Beteiligt an alle anzeigen Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software On the Prospects of Using Machine Learning for the Numerical Simulation of PDEs: Training Neural Networks to Assemble Approximate Inverses Fundamentals of a numerical cloud computing for applied sciences : Preparing cloud computing for "Simulation-as-a-Service" How applied sciences can accelerate the energy revolution : A pleading for energy awareness in scientific computing The ICARUS white paper. A scalable energy-efficient, solar-powered HPC center based on low power GPUs FFF2: future-proof high performance numerical simulation for CFD with FEASTFLOW (2) A simulation suite for Lattice-Boltzmann based real-time CFD applications exploiting multi-level parallelism on modern multi- and many-core architectures Efficient finite element geometric multigrid solvers for unstructured grids on GPUs Towards a complete FEM-based simulation toolkit on GPUs : geometric multigrid solvers Towards a complete FEM-based simulation toolkit on GPUs : unstructured grid finite element geometric multigrid solvers with strong smoothers based on sparse approximated inverses Lattice-Boltzmann simulation of the shallow water equations with fluid structure interaction on multi- and manycore processes

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Katalog der Deutschen Nationalbibliothek

Markus Geveler

Basic Machine Learning Approaches for the Acceleration of PDE Simulations and Realization in the FEAT3 Software

On the Prospects of Using Machine Learning for the Numerical Simulation of PDEs: Training Neural Networks to Assemble Approximate Inverses

Fundamentals of a numerical cloud computing for applied sciences : Preparing cloud computing for "Simulation-as-a-Service"

How applied sciences can accelerate the energy revolution : A pleading for energy awareness in scientific computing

The ICARUS white paper. A scalable energy-efficient, solar-powered HPC center based on low power GPUs

FFF2: future-proof high performance numerical simulation for CFD with FEASTFLOW (2)

A simulation suite for Lattice-Boltzmann based real-time CFD applications exploiting multi-level parallelism on modern multi- and many-core architectures

Efficient finite element geometric multigrid solvers for unstructured grids on GPUs

Towards a complete FEM-based simulation toolkit on GPUs : geometric multigrid solvers

Towards a complete FEM-based simulation toolkit on GPUs : unstructured grid finite element geometric multigrid solvers with strong smoothers based on sparse approximated inverses

Lattice-Boltzmann simulation of the shallow water equations with fluid structure interaction on multi- and manycore processes