SBI-FAIR

Computational Science is being revolutionized by the integration of AI and simulation and in particular, by deep learning surrogates that can replace all or part or of traditional large-scale HPC computations. Surrogates can achieve remarkable performance improvements (e.g., several orders of magnitude) and so save in both time and energy. The Surrogate Benchmark Initiative (SBI) project will create a community repository and FAIR data ecosystem for HPC application surrogate benchmarks, including data, code, and all relevant collateral artifacts the science and engineering community needs to use and reuse these data sets and surrogates. We intend that our repositories will generate active research from both the participants in our project and the broad community of AI and domain scientists. By collaborating with the major industry organization in this area -- MLPerf -- and mirroring their process as much as possible, we will both increase the value of and obtain industry involvement in..

Also known as: SBI FAIR Authors, SBI FAIR Project

Primary location: San Diego United States