Abstract
flowTorch - a Python library for analysis and reduced-order modeling of fluid flows
Highlights
Popular data formats for fluid flows like OpenFOAM, VTK, or DaVis may be accessed via a common interface in a few lines of Python code
The data are organized as PyTorch tensors
The flowTorch packages includes a collection of Jupyter notebooks demonstrating how to apply the library components in a variety of different use cases, e.g., finding coherent flow structures with modal analysis or creating reduced-order models
Summary
The flowTorch library enables researchers to access, analyze, and model fluid flow data from experiments or numerical simulations. Instead of a black-box end-to-end solution, flowTorch provides modular components allowing to assemble transparent and reproducible workflows with ease. Popular data formats for fluid flows like OpenFOAM, VTK, or DaVis may be accessed via a common interface in a few lines of Python code. The data are organized as PyTorch tensors. Relying on PyTorch tensors as primary data structure enables fast array operations, parallel processing on CPU and GPU, and exploration of novel deep learning-based analysis and modeling approaches. The flowTorch packages includes a collection of Jupyter notebooks demonstrating how to apply the library components in a variety of different use cases, e.g., finding coherent flow structures with modal analysis or creating reduced-order models
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