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Quantifying Hidden Order out of Equilibrium

机译：量化隐藏订单超出均衡

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摘要

While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of nonequilibrium phenomena in diverse settings, from glasses to driven systems to biology. The length of a losslessly compressed data file is a direct measure of its information content: The more ordered the data file is, the lower its information content and the shorter the length of its encoding can be made. Here, we describe how data compression enables the quantification of order in nonequilibrium and equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown. We consider absorbing state models on and off lattice, as well as a system of active Brownian particles undergoing motility-induced phase separation. The technique reliably identifies nonequilibrium phase transitions, determines their character, quantitatively predicts certain critical exponents without prior knowledge of the order parameters, and reveals previously unknown ordering phenomena. This technique should provide a quantitative measure of organization in condensed matter and other systems exhibiting collective phase transitions in and out of equilibrium.
机译：虽然通过统计力学良好地描述了相互作用，状态和相位转变，但缺乏合适的状态参数已经阻碍了在不同环境中对不合格现象的理解，从玻璃到驱动系统到生物学。无损压缩数据文件的长度是其信息内容的直接测量：数据文件越多，其信息内容的越低，并且可以进行编码的长度。在这里，我们描述了数据压缩如何在非QuibiRim和均衡的许多身体系统中定量顺序，即使是底层的顺序形式未知。我们考虑在晶格上吸收状态模型，以及经历动力诱导的相分离的活性褐色颗粒系统。该技术可靠地识别非识基阶段转换，确定其特征，定量地预测某些关键指数而无需先验知识的订单参数，并揭示先前未知的订购现象。该技术应提供凝聚体和其他系统中的组织的定量衡量，表现出集体阶段过渡进出平衡。

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