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Articles by Andreas Knüpfer
Computer Science > Artificial Intelligence
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Benchmarking universal machine learning interatomic potentials on elemental systems
Abstract The rapid emergence of universal machine learning interatomic potentials (uMLIPs) has transformed materials modeling. Nevertheless, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge.
Prediction of Steady and Unsteady Flow Quantities Using Multiscale Graph Neural Networks
Analysis, optimization and uncertainty quantification of the aerodynamic behaviour of turbomachinery components is a fundamental part of the current industrial design process and requires the extensive use of compute-intensive CFD simulations. In this paper we investigate whether graph neural networks can be useful as surrogate models to accelerate the design process, for example in a multi-fidelity framework.
[2307.13592] Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes
[Submitted on 25 Jul 2023] Title:Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes Download a PDF of the paper titled Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes, by Sebastian Str\"onisch and 3 other authors Download PDF Abstract: Mesh-based numerical solvers are an important part in many design tool chains.
A Halo abstraction for distributed n-dimensional structured grids within the C++ PGAS library DASH
A Halo abstraction for distributed n-dimensional structured grids within the C++ PGAS library DASH ZIH, Technische Universität Dresden, Dresden, Deutschland DOI 10.7717/peerj-cs.1203 Published 2023-02-03 Accepted 2022-12-13 Received 2022-05-13 Academic Editor Daniele D’Agostino Subject Areas Distributed and Parallel Computing, Software Engineering Keywords C++, DASH, PGAS, Halo, Stencil codes, Distributed memory, Abstraction Copyright © 2023 Hünich and Knüpfer Licence This is an open access...
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