【摘 要】
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I will discuss a data-driven stochastic approach to modeling unresolved scales in multiscale systems,in which feedback from micro-scales is represented by a
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I will discuss a data-driven stochastic approach to modeling unresolved scales in multiscale systems,in which feedback from micro-scales is represented by a network of Markov processes.The Markov processes are conditioned on macro-scale model variables,and their properties are inferred from pre-computed high-resolution(micro-scale resolving)simulations.These processes are designed to emulate,in a statistical sense,the feedback observed in the high-resolution simulations,thereby providing a statisticaldynamical coupling between micro-and macro-scale models.
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