7 research outputs found
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data
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.Comment: Accepted in Advances in Neural Information Processing Systems 36
(NeurIPS 2023). 55 pages, 21 figures. v2: Corrected co-author name. Keywords:
Super-resolution, 3D, Neural Scaling, Physics-informed Loss, Computational
Fluid Dynamics, Partial Differential Equations, Turbulent Reacting Flows,
Direct Numerical Simulation, Fluid Mechanics, Combustio