Mean field asymptotic for stochastic processes on graphs

在过去的三十年里，物理学家已经开发出复杂的非严格技术，用于精确预测大型复杂（随机）系统的渐近行为。数学家在发展相应的严格理论和证明这些预言方面取得了重大进展。概率论是在这种收敛的最前沿，开始与理论的大偏差和继续与新兴的充满活力的活动在研究随机动力学的相互作用的粒子，大型随机矩阵，吉布斯措施和平面物体的共形对称性。从物理学的角度来看，一个特别成功的故事是平均场（无序）模型，其中通常存在两个层次的随机性，其中一个被冻结（淬火），以依赖图或权重的形式产生随机过程，其分布是可交换的，当平均超过无序。我们计划追求的具体方向是（1）严格研究离散变量的大系统，这些系统根据由以下集合确定的平均场模型强烈相互作用：（2）随机矩阵结构模型谱的渐近行为与相应的随机图和图嵌入的性质之间的新联系，（3）自旋系统失平衡时的随机动力学性质，如老化和与其相变相关的有限尺寸标度。该项目预计将产生新的数学思想和技术，解决概率论中具有挑战性的问题，影响其与统计物理的接口，并为大型约束满足网络的典型行为分析提供见解，从而推进NSF网络支持的发现和创新计划的核心进展。预计对离散概率和信息理论学生的毕业生和研究生培训也会产生长期影响。

Over the past three decades, physicists have developed sophisticated non-rigorous techniques for accurately predicting the asymptotic behavior of large complex(random) systems. Mathematicians are making significant progress in developing the corresponding rigorous theories and proving these predictions. Probability theory is at the forefront of this convergence, starting with the theory of large deviations and continuing with the emerging vibrant activity in the study of stochastic dynamics of interacting particles, large random matrices, Gibbs measures and planar objects with conformal symmetries. A particular success story from a physics point of view are mean field(disordered) models, where two levels of randomness are often present, one of whom frozen (quenched), in the form of dependence graph or weights for the resulting stochastic process, whose distribution is exchangeable when averaged over the disorder. Specific directions we plan to pursue are (1) Rigorous study of large systems of discrete variables that are strongly interacting according to a mean field model determined by an ensemble of (randomly chosen) graphs, (2) novel connections between the asymptotic behavior of the spectrum of structured models of random matrices and properties of the corresponding random graphs and graph embeddings,(3) properties of stochastic dynamics for spin systems out of equilibrium, such as aging and the finite size scaling associated with their phase transitions.This project is expected to yield new mathematical ideas and techniques that resolve challenging problems in probability theory, impact its interface with statistical physics, and provide insight to the analysis of typical behavior of large constraint satisfaction networks, thus advancing progress at the core of the NSF cyber-enabled discovery and innovation initiative. Long-term effects on graduate and post-graduate training of students in discrete probability and information theory are also expected.