【摘 要】
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We discuss ATLAS,a statistical learning framework for model reduction of high-dimensional dynamical systems with few intrinsic degrees of freedom.The algorithm is highly parallelizable and only requir
【机 构】
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Duke Univ. Univ.of Chicago
论文部分内容阅读
We discuss ATLAS,a statistical learning framework for model reduction of high-dimensional dynamical systems with few intrinsic degrees of freedom.The algorithm is highly parallelizable and only requires short trajectories of the system(treated as a black-box),and learns from these short paths an ensemble of accurate local reduced models.
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