NSF POSE: Phase II: BLASTNet: An Open-Source Ecosystem for Integrating Decentralized Scientific Machine Learning with Flow Physics

Award ID: 2345740

Flow physics is a field of study essential to advancing applications within climate, energy, and biomedical domains. In this field, the establishment of open-source machine learning (ML) resources can accelerate the development of modeling tools and fundamental understanding that can guide government policy and improve performance of engineering systems. However, the lack of publicly available datasets and an open-source ecosystem (OSE) represents major obstacles to advance these data-driven methods in reproducible ways. By addressing this need, the objective of this project is to expand a resource-efficient open-source framework, namely the Bearable Large Accessible Scientific Training Network (BLASTNet), into a fully sustainable OSE of contributors who generate and share public ML models, methods, and code, as well as high-fidelity, flow-physics datasets, on a decentralized platform for open community access. To transition BLASTNet into a fully sustainable OSE, this integrated research addresses the (i) open-citizen science activities, organization of outreach efforts, and external partnerships for growing BLASTNet into a self-sustained community, (ii) continuous improvement of the diversity of datasets, code, and models within BLASTNet via external contributions, and (iii) maintenance of automation capabilities by leveraging data-transfer services and utilizing open-data repositories that can sustain the growth of the community and contributed resources. The BLASTNet OSE will directly impact reproducibility issues and accelerate ML research across various flow-physics domains, including hypersonic, geophysical, atmospheric, and biomedical flows. Best practices and ideas on open science disseminated through BLASTNet will influence open and reproducible science in other research domains. In addition, outreach events in collaboration with the Women in Data Science Worldwide, will lead to a diverse community that encourages the participation of traditionally under-represented groups within science, engineering, and ML. The open participation model fosters an inclusive environment that will be effective for disseminating science to all regardless of background and education level. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Florence Rabanal