Biosciences Area

Advancing Watershed Understanding through Exascale Simulation and Machine Learning

ExaSheds is a new project led by Berkeley Lab PI Carl Steefel of the Earth and Environmental Science Area (EESA) and Oak Ridge National Lab co-PI Scott Painter. It represents the first systematic effort to leverage powerful machine learning and exascale computing, applied to ever-larger and more-complex data obtained from watershed field observations, to gain a predictive understanding of watershed behavior. The project is funded by DOE Biological and Environmental Research and will initially take advantage of datasets being collected at the East River, Colorado watershed site, which has been developed as part of Berkeley Lab’s DOE Watershed Function Science Focus Area (SFA). The interdisciplinary research team includes Environmental Genomics and Systems Biology (EGSB) Division co-deputy Ben Brown, as well as partners at Lawrence Livermore and Pacific Northwest National Labs.

Read more from EESA.

New Machine Learning Method Sees the Forests and the Trees

While it may be the era of supercomputers and big data, without smart methods to mine all that data, it’s only so much digital detritus. Now researchers at Berkeley Lab and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time.

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