BLASTNetv3: Multiphysics Simulation Dataset
Abstract
Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.
Authors 37
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Stanford University
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Sandia National Laboratories California
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Sandia National Laboratories California
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Sandia National Laboratories California
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Sandia National Laboratories California
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Lawrence Livermore National Laboratory
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Lawrence Livermore National Laboratory
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Stanford University
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The University of Melbourne
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The University of Melbourne
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Polytechnique Montréal
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University of Connecticut
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Stanford University
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Stanford University
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Google (United States)
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Google (United States)
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Google (United States)
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Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique
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CERFACS
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Nvidia (United Kingdom) · Nvidia (United States)
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NVIDIA
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Michael Gauding Aachen
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RWTH Aachen University
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Technische Universität Ilmenau
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Technische Universität Ilmenau
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Forschungszentrum Jülich
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Technische Universität Ilmenau
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Technische Universität Ilmenau
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Stanford University
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Harvard University
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Google (United States)
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Georgia Institute of Technology
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Georgia Institute of Technology
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Georgia Institute of Technology
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Georgia Institute of Technology
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University of Houston
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Kyoto University
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Kyoto University
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Kyoto University
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University at Buffalo, State University of New York
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University at Buffalo, State University of New York
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Embry-Riddle Aeronautical University Worldwide & Online
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Embry-Riddle Aeronautical University Worldwide & Online
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Universitat Politècnica de Catalunya
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Universitat Politècnica de Catalunya
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Universitat Politècnica de Catalunya
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Universitat Politècnica de Catalunya
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Universitat Politècnica de Catalunya
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Universitat Politècnica de Catalunya
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Otto-von-Guericke-Universität Magdeburg
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Otto-von-Guericke-Universität Magdeburg
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