Fostering Open-source Resources and Practices within Deep Learning of Flow Physics

In the age of big data, open-source platforms and culture offer new resources and approaches for scientific investigations. In this work, we present pathways for integrating open-source techniques involving public datasets and crowd-sourcing into the study of multi-physics flow phenomena. One such pathway involves the Bearable Large Accessible Scientific Training Network-of-Datasets (BLASTNet) - at https://blastnet.github.io/ - which contains terabytes of non-reacting and reacting flow physics data, shared via community involvement on a free cloud computing/storage platform (Kaggle). We demonstrate that this resource can be readily employed for a wide range of data-driven tasks including closure modeling, dimensionality reduction, and super-resolution. We also demonstrate that BLASTNet can be employed for introducing new ideas from other machine learning communities such as ML model benchmarking and data competitions. Results from this work indicate that these open source techniques can help accelerate the development of deep learning techniques within flow physics.