Many collective systems exist in nature far from equilibrium, ranging from
cellular sheets up to flocks of birds. These systems reflect a form of active
matter, whereby individual material components have internal energy. Under
specific parameter regimes, these active systems undergo phase transitions
whereby small fluctuations of single components can lead to global changes to
the rheology of the system. Simulations and methods from statistical physics
are typically used to understand and predict these phase transitions for
real-world observations. In this work, we demonstrate that simulation-based
inference can be used to robustly infer active matter parameters from system
observations. Moreover, we demonstrate that a small number (from one to three)
snapshots of the system can be used for parameter inference and that this
graph-informed approach outperforms typical metrics such as the average
velocity or mean square displacement of the system. Our work highlights that
high-level system information is contained within the relational structure of a
collective system and that this can be exploited to better couple models to
data.

本文研究了非平衡态下存在的集体系统，详细探讨了个体材料组分内部能量以及小扰动对整个系统流变性质的全球影响，利用模拟推断方法依据少量图像数据提取了集体系统参数的可行性，着重强调了复杂集体系统内部的结构关系是生成模型和数据更精确匹配的重要因素。

基于图形的主动物质模型仿真推断

Graph-informed simulation-based inference for models of active matter

A variety of computational models have been developed to describe active
matter at different length and time scales. The diversity of the methods and
the challenges in modeling active matter---ranging from molecular motors and
cytoskeletal filaments over artificial and biological swimmers on microscopic
to groups of animals on macroscopic scales---mainly originate from their
out-of-equilibrium character, multiscale nature, nonlinearity, and multibody
interactions. In the present review, various modeling approaches and numerical
techniques are addressed, compared, and differentiated to illuminate the
innovations and current challenges in understanding active matter. The
complexity increases from minimal microscopic models of dry active matter
toward microscopic models of active matter in fluids. Complementary,
coarse-grained descriptions and continuum models are elucidated. Microscopic
details are often relevant and strongly affect collective behaviors, which
implies that the selection of a proper level of modeling is a delicate choice,
with simple models emphasizing universal properties and detailed models
capturing specific features. Finally, current approaches to further advance the
existing models and techniques to cope with real-world applications, such as
complex media and biological environments, are discussed.

本文介绍了各种建模方法和数值技巧，以阐明理解主动物质的创新和当前挑战。本文还认为微观细节通常是相关且强烈影响集体行为的，这意味着选择适当的建模水平是一个微妙的选择。最终，我们将讨论进一步改进现有模型和技术以应对复杂环境和生物环境的当前方法。

主动物质的计算模型

Computational models for active matter

Epithelial cell monolayers show remarkable displacement and velocity
correlations over distances of ten or more cell sizes that are reminiscent of
supercooled liquids and active nematics. We show that many observed features
can be described within the framework of dense active matter, and argue that
persistent uncoordinated cell motility coupled to the collective elastic modes
of the cell sheet is sufficient to produce swirl-like correlations. We obtain
this result using both continuum active linear elasticity and a normal modes
formalism, and validate analytical predictions with numerical simulations of
two agent-based cell models, soft elastic particles and the self-propelled
Voronoi model together with in-vitro experiments of confluent corneal
epithelial cell sheets. Simulations and normal mode analysis perfectly match
when tissue-level reorganisation occurs on times longer than the persistence
time of cell motility. Our analytical model quantitatively matches measured
velocity correlation functions over more than a decade with a single fitting
parameter.

研究表明，上皮细胞单层在长度超过十个细胞大小的距离上表现出类似于超冷液体和活性向列相的位移和速度相关性，这可以在密集活性物质的框架下进行描述，并且通过连续活性线性弹性学和模态分析证实，模拟和模态分析在组织层次时空重组时间大于细胞运动持续时间时是完美匹配的。