Mathematical and Computational Methods for Non-Equilbrium Systems

这个研究项目关注的是受随机影响的复杂系统的时间行为。 具有大量参数和媒介的复杂系统是无处不在的，例如，高速公路网络上的交通或生化系统，其中涉及许多类型的分子和许多化学反应。 对于这样的系统，敏感性分析的问题提出了特殊的挑战：敏感性分析在于了解参数对系统未来行为的影响-例如，在忙碌上关闭一条车道如何影响交通，可能是几英里外和几个小时后。 了解这些影响是预测建模领域的核心，并提出了巨大的挑战，这些挑战往往超出了现代计算机的能力。 主要研究人员将开发概念和计算工具，以互补的方式使用来自不同科学领域的技术来解决这些问题。 这包括信息论的方法（它量化了信息在物理过程中如何丢失），来自统计力学（这使得人们能够理解非常大的系统的全局行为），并从数值分析（这是一门允许人们在计算机上模拟和再现复杂系统行为的科学）。理解由外力驱动的非平衡系统，边界效应，多物理场提出了许多数学和计算挑战，特别是如果系统很大并且涉及许多参数。 一个典型的例子是生化反应网络，它通常涉及多个时间尺度，反馈回路，数百个物种和数百个参数。 该项目的核心是开发数学和数值工具，用于分析和模拟这种非平衡高维随机系统。 该研究融合了信息论，统计力学，数值分析和概率论的概念，以开发新的算法，以及评估复杂系统现有算法的新工具。 敏感性分析问题在这项工作中起着核心作用。 这里的新颖之处之一是系统地使用和发展的信息理论工具的分析和随机系统的不确定性量化，特别强调系统的长期行为。 相对熵和费雪信息矩阵等概念，特别适用于系统的时间历程，特别适合。 该项目的另一个重要特点是，非平衡系统不尊重时间反演下的不变性。 定量测量这种对称性破缺是现代非平衡统计力学的主要主题之一，主要研究人员将使用本项目中开发的概念来创建有效的算法，以便为复杂系统做到这一点。 最后，研究小组将研究记忆或延迟效应的作用，这在具有多个空间或时间尺度的复杂系统中是不可避免的。

This research project is concerned with the behavior in time of complex systems subject to random influences. Complex systems with a large number of parameters and agents are ubiquitous -- think, for example, of the traffic on a network of highways or of bio-chemical systems, which involve many types of molecules and many chemical reactions. For such systems, the issue of sensitivity analysis presents special challenges: sensitivity analysis consists in understanding the effects of parameters on the future behavior of the system -- for example, how the closure of one lane on a busy highway influences the traffic, possibly many miles away and several hours later. Understanding these effects is at the heart of the field of predictive modeling and poses formidable challenges, which often exceed the capabilities of modern computers. The Principal Investigators will develop tools, both conceptual and computational, to tackle such issues by using, in a complementary fashion, techniques from different fields of science. This includes methods from information theory (which quantifies how information is lost in physical processes), from statistical mechanics (which allows one to understand the global behavior of very large systems), and from numerical analysis (which is the science that allows one to simulate and reproduce the behavior of complex systems on a computer).Understanding non-equilibrium systems driven by external forces, boundary effects, and multi-physics presents numerous mathematical and computational challenges, especially if the system is large and involves many parameters. A paradigmatic example is a bio-chemical reaction network, which often involves multiple time scales, feedback loops, hundreds of species, and hundreds of parameters. At the center of this project is the development of mathematical and numerical tools for the analysis and simulation of such non-equilibrium high-dimensional stochastic systems. The research blends together concepts from information theory, statistical mechanics, numerical analysis, and probability theory in order to develop novel algorithms, as well as novel tools to assess existing algorithms for complex systems. The issue of sensitivity analysis plays a central role in the work. One of the novelties here is the systematic use and development of information-theoretic tools for the analysis and uncertainty quantification of stochastic systems, with a strong emphasis on the long-time behavior of the systems. Concepts such as the relative entropy and the Fisher information matrix, especially applied to the time histories of the system, are particularly well suited. Another important feature of the project concerns the fact that non-equilibrium systems do not respect invariance under time reversal. Measuring quantitatively this symmetry breaking is one the main themes in modern non-equilibrium statistical mechanics, and the Principal Investigators will use concepts developed in this project to create efficient algorithms to do this for complex systems. Finally, the research team will investigate the role of memory or delay effects, which are unavoidable in complex systems with multiple spatial or temporal scales.