Dynamics, aging and universality in complex systems

该奖项支持将于2017年6月19日至23日在纽约市的柯朗研究所（NYU）举行的“复杂系统中的动力学、老化和普遍性”会议。会议有30个确认发言人在非常活跃和基本领域的概率，分析和数学物理的交叉点。对复杂无序系统进行严格研究的主要目的是了解随机微观系统如何表现出可预测的和统计上普遍的集体宏观行为。许多这样的系统演化到稳态，但是在非常长的时间尺度上这样做（例如，系统大小的指数）。这些复杂系统中的动力学和老化试图了解它们复杂的能量景观如何控制它们的前稳态行为。在这一领域有许多不同的方法将在会议上代表，包括自旋玻璃，随机偏微分方程（PDE），大偏差理论和随机矩阵理论。这次会议将导致这些领域之间的交叉施肥，以及激励和通知新一代的研究人员。本次会议将吸引众多不同领域的参与者，包括许多早期的职业研究人员，妇女和代表性不足的少数民族。本次会议将讨论的许多复杂系统旨在描述真实的世界现象，并为数学模型证明的结果提供适用于真实的系统的预测。在一个更简单的层面上，例如，考虑独立随机变量和的中心极限定理如何预测高斯统计，高斯统计驱动了真实的世界中的大部分统计。除了物理应用之外，复杂系统（例如，自旋玻璃，生长过程和随机矩阵）在计算机科学，机器学习，数据科学，生物信息学，化学，甚至生态学和地球科学等领域都有许多应用。本次会议的网站是：http://cims.nyu.edu/conferences/gba60/

This award supports the conference "Dynamics, Aging and Universality in Complex Systems" that will be held from the 19th-23rd of June, 2017, at the Courant Institute (NYU) in New York City. The conference has 30 confirmed speakers in very active and fundamental areas at the intersection of probability, analysis, and mathematical physics. The main aim in the rigorous study of complex disordered systems is to understand how random microscopic systems display predictable and statistically universal collective macroscopic behaviors. Many such systems evolve to stationary states, but do so in very long time scales (e.g., exponential in system size). Dynamics and aging in these complex systems seeks to understand how their complicated energy landscapes governs their pre-stationary behavior. There are many different approaches in this area that will be represented at the conference, including spin-glasses, stochastic partial differential equations (PDEs), large deviation theory, and random matrix theory. This gathering will lead to cross-fertilization between these areas as well as inspire and inform a new generation of researchers. This event will attract a large and diverse spectrum of participants, including many earlier career researchers, women and under-represented minorities.Many of the complex systems which will be discussed in this conference aim to describe real world phenomena and results proved for the mathematical models provide predictions applicable to the real systems. At a simpler level, think, for instance, of how the central limit theorem for sums of independent random variables predicts the Gaussian statistics which drives a large part of statistics in the real world. Besides physical applications, complex systems (e.g., spin glasses, growth processes and random matrices) have found many applications in computer science, machine learning, data science, bioinformatics, chemistry, and even areas like ecology and earth science.The website for this conference is: http://cims.nyu.edu/conferences/gba60/