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Weinan E: "Machine learning based multi-scale modeling"

Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Machine learning based multi-scale modeling" Weinan E - Princeton University, Mathematics Abstract: We will discuss a general methodology for developing reliable and interpretable coarse-grained models for multi-scale problems. This methodology involves two interactive components: Adaptive generation of data and extracting multi-scale models based on the data. Of particular interest are the treatment of physical constraints and the reliability of the machine learning-based models. Applications to kinetic theory and coarse-grained molecular dynamics will be discussed. Institute for Pure and Applied Mathematics, UCLA October 15, 2019 For more information: http://www.ipam.ucla.edu/mlpws2