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Researchers studying climate, astrophysics, and high-energy physics use large, complex simulations to guide experiments and test theories. These compute-intensive programs can run much faster using emulators that approximate some aspects of those models. Recently, scientists have started creating “learned emulators” using AI neural network approaches, but have not yet fully explored the advantages and potential pitfalls of these surrogates. These aspects will be examined by the team of Rebecca Willett, professor of computer science and statistics at UChicago, Dana Mendelson, assistant professor of mathematics at UChicago, Prasanna Balaprakash, computer scientist at Argonne, Jiali Wang, assistant atmospheric scientist in Environmental Science Division at Argonne, and Rao Kotamarthi, department head of atmospheric science and climate and chief scientist for the Environmental Science Division at Argonne. This team will explore the mathematical limits of these methods and quantify key trade offs related to accuracy and speed.


Rebecca Willett

Professor of Computer Science and Statistics, University of Chicago

Dana Mendelson

Assistant Professor of Mathematics, University of Chicago

Prasanna Balaprakash

Computer Scientist, Argonne National Laboratory

Jiali Wang

Assistant Atmospheric Scientist, Environmental Science Division, Argonne National Laboratory

Rao Kotamarthi

Department Head of Atmospheric Science and Climate and Chief Scientist for the Environmental Science Division, Argonne National Laboratory