About
Click here for detailed papers on this work!
To download all cases via Kaggle API, download this bash script.
Summary of the data are avalable here!
Mission
BLASTNet aims to address gaps in open machine learning (ML) within the sciences, specifically fluid mechanics by providing researchers in reacting and non-reacting flow physics communities with (mostly) externally contributed open-source ML resources.
These contributions now include (i) 4.8 TB of high-fidelity simulation datasets that have been processed in a convenient format for ML applications, (ii) >13,000 lines of code that aid the training and evaluating of these models, (iii) >100 pre-trained weights in flow physics problems, and (iv) regular workshop events that disseminate ML for flow physics via seminars and competitions.
Application
This data is useful for fluid flows in a wide range of ML applications tied to automotive, propulsion, energy, and the environment. Specifically, scientific engineering tasks related to these domains may include turbulent closure modeling, spatio-temporal modeling, and inverse modeling.
Distribution
Our ML resources are shared via github and Kaggle. Specifically, code is shared via github, while data and models are shared via Kaggle.
To circumvent Kaggle storage constraints, we partition our data into a network of <100 GB subsets, with each subset containing a separate simulation configuration. This partitioned data can then be uploaded as separate datasets on Kaggle. To download all cases via Kaggle API, download this bash script. Summary of the data are avalable here!
Our network of datasets approach:
Contribute
Find out how to contribute to our project to join our growing list of authors.
Authors and Contributors
We thank the following people for contributing and curating this network-of-datasets:
| Name | Affilliation | Date Joined |
|---|---|---|
| Wai Tong Chung | Stanford University | June 6 2022 |
| Matthias Ihme | Stanford University, SLAC National Laboratory | June 6 2022 |
| Ki Sung Jung | Sandia National Laboratory | June 6 2022 |
| Jacqueline H. Chen | Sandia National Laboratory | June 6 2022 |
| Jack Guo | Stanford University | June 6 2022 |
| Davy Brouzet | Stanford University | June 6 2022 |
| Mohsen Talei | University of Melbourne | June 6 2022 |
| Bin Jiang | University of Melbourne | Nov 18 2022 |
| Bruno Savard | Polytechnique Montréal | Jan 26 2023 |
| Alexei Y. Poludnenko | University of Connecticut | June 7 2023 |
| Bassem Akoush | Stanford University | June 7 2023 |
| Pushan Sharma | Stanford University | June 7 2023 |
| Alex Tamkin | Stanford University | June 7 2023 |
| Qing Wang | August 29 2023 | |
| Shantanu Shahane | August 29 2023 | |
| Yifan Chen | August 29 2023 |