摘要翻译：
我们提出了一个通用的数值方案，用于统计矩闭包的实际实现，适用于复杂的，大规模的，非线性系统的建模。在最近发展起来的无方程方法的基础上，这种方法对封闭动力学进行了数值积分，而封闭动力学的方程可能不是封闭形式的。尽管闭包动力学引入了未知有效性的统计假设，但它们可以具有显著的计算优势，因为它们通常具有较少的自由度，并且可能比原始的详细模型更不僵硬。闭包方法原则上可以应用于广泛的非线性问题，包括不存在尺度分离的强耦合系统（确定性或随机性）。我们证明了在非线性随机偏微分方程上实现基于熵的Eyink-Levermore闭包的无方程方法。
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英文标题：
《Equation-free implementation of statistical moment closures》
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作者：
Francis J. Alexander, Gregory Johnson, Gregory L. Eyink, and Ioannis
  G. Kevrekidis
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最新提交年份：
2007
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分类信息：

一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Physics        物理学
二级分类：Other Condensed Matter        其他凝聚态物质
分类描述：Work in condensed matter that does not fit into the other cond-mat classifications
在不适合其他cond-mat分类的凝聚态物质中工作
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英文摘要：
  We present a general numerical scheme for the practical implementation of statistical moment closures suitable for modeling complex, large-scale, nonlinear systems. Building on recently developed equation-free methods, this approach numerically integrates the closure dynamics, the equations of which may not even be available in closed form. Although closure dynamics introduce statistical assumptions of unknown validity, they can have significant computational advantages as they typically have fewer degrees of freedom and may be much less stiff than the original detailed model. The closure method can in principle be applied to a wide class of nonlinear problems, including strongly-coupled systems (either deterministic or stochastic) for which there may be no scale separation. We demonstrate the equation-free approach for implementing entropy-based Eyink-Levermore closures on a nonlinear stochastic partial differential equation.
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PDF链接：
https://arxiv.org/pdf/704.0804