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arXiv 1201.4601math.APmath-phmath.DSmath.MPmath.PR
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大偏差和梯度流

Large deviations and gradient flows

Stefan Adams, Nicolas Dirr, Mark A. Peletier, Johannes Zimmer

摘要:

在最近的工作[1]中,我们发现了奥托将扩散描述为熵梯度流[16]与描述微观图像(布朗运动)的大偏差原理之间的有趣联系。在本文中,我们勾勒出这种连接,显示它如何推广到更广泛的系统类,并评论的后果和影响。具体来说,我们连接宏观梯度流与大偏差原则,并指出一个更大的图片出现的潜力:我们指出,在一些非平衡的情况下,熵和热力学自由能可以通过大偏差原则。这里所提倡的方法不同于已建立的水动力极限通过,而是扩展了平衡情况下众所周知的一种联系。

英文摘要:

In recent work [1] we uncovered intriguing connections between Otto's characterisation of diffusion as entropic gradient flow [16] on one hand and large-deviation principles describing the microscopic picture (Brownian motion) on the other. In this paper, we sketch this connection, show how it generalises to a wider class of systems, and comment on consequences and implications. Specifically, we connect macroscopic gradient flows with large deviation principles, and point out the potential of a bigger picture emerging: we indicate that in some non- equilibrium situations, entropies and thermodynamic free energies can be derived via large deviation principles. The approach advocated here is different from the established hydrodynamic limit passage but extends a link that is well known in the equilibrium situation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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