CAREER: Connecting Mathematical Models Across Scales

职业：跨尺度连接数学模型

• 批准号: 1753357
• 负责人: Mark Transtrum
• 金额: $ 58.5万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1753357&HistoricalAwards=false
• 关键词: CAREER; Connecting; Mathematical; Models; Across

NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research aimed at developing simple models of complex systems. Complex systems are made up of a large number of smaller components that interact in ways that can lead to interesting collective behaviors where the components act in concert. Examples of such systems include materials, biological pathways, and neural networks as well as many engineered systems and social networks. Complex systems are difficult to model. Usually, mathematical models are most useful when they are sufficiently simple to capture the relevant parts of the phenomenon of interest while ignoring irrelevant details. For complex systems, it is difficult to know from the start which components are relevant and which are irrelevant. Consequently, models of real-world systems tend to be overly complicated, difficult to work with, and have limited predictive power compared to more parsimonious representations.This project leverages recent advances that combine the mathematical areas of information theory with differential geometry and topology. The basic idea utilizes a systematic method for pruning irrelevant complications from a model of a complex system until a sufficiently simple model is obtained. By enumerating all of the resulting approximations, the scientist has a roadmap from the complicated, detailed representation of the physical system, through various types of approximations and the system behaviors they describe. Put another way, the process acts as a mathematical bridge across scales: connecting microscopic mechanisms to systems-level phenomena. In this project, the PI will apply these new mathematical and associated computational tools to three target application areas: crystal structures of alloys, biochemical kinetics of developmental pathways, and networks of neurons. By better understanding how mathematical models reflect relevant physical details, models will be better at predicting the behavior of complex systems and enable more sophisticated design and control of complicated materials and processes. This work also includes an educational component that will develop a pedagogy of interdisciplinary science. This pedagogy will focus on university faculty in the form of a seminar series; university students in the form of a multi-department, special topics course; and high school teachers in the form of teaching workshops and web resources for high school science teachers.TECHNICAL SUMMARY This CAREER award supports theoretical and computational research aimed at developing simple models of complex systems. Simple mathematical models have always played an important role in scientific inquiry. For systems with separated scales or symmetries, there are standard techniques for constructing parsimonious representations from complicated, mechanistic descriptions. Recent advances combining information theory and differential geometry and topology suggest new methods for reasoning about the relationship between mechanisms and phenomena in models of complex physical systems. For this research, the PI takes the approach that the predictions of a multi-parameter model form a manifold embedded in an abstract data space. It has been observed that typical model manifolds are bounded and that simple, approximate models reside on the boundary of the model manifold. The Manifold Boundary Approximation Method constructs a sequence of limiting approximations, that is, "small parameters" that corresponds to a sequence of simple, approximate models. This research will extend these results to three target application areas: alloy crystal structure, biochemical kinetics of developmental pathways, and networks of neurons. Within these target application areas, the PI will develop a new approach to modeling based on the concept of a "supremum model", that is, the minimal model that simultaneously explains several behaviors. When combined with the Manifold Boundary Approximation Method, the supremum model concept enables one to bootstrap from several models of overlapping system components to models of much larger systems, the direct exploration of which would be computationally challenging or infeasible.This work also includes an educational component that will develop a pedagogy of interdisciplinary science. This pedagogy will target university faculty in the form of a seminar series; university students in the form of a multi-department, special topics course; and high school teachers in the form of teaching workshops and web resources for high school science teachers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术总结这个职业奖支持旨在开发复杂系统的简单模型的理论和计算研究。 复杂系统由大量较小的组件组成，这些组件以某种方式相互作用，可以导致有趣的集体行为，其中组件协同行动。 这些系统的例子包括材料、生物通路和神经网络，以及许多工程系统和社交网络。 复杂的系统很难建模。 通常情况下，数学模型是最有用的，当它们足够简单，以捕捉感兴趣的现象的相关部分，而忽略不相关的细节。 对于复杂的系统，很难从一开始就知道哪些组件是相关的，哪些是不相关的。 因此，现实世界系统的模型往往过于复杂，难以处理，并具有有限的预测能力相比，更简约的representations.This项目利用最近的进展，结合联合收割机的数学领域的信息理论与微分几何和拓扑结构。 其基本思想是利用一种系统的方法，从一个复杂系统的模型中修剪不相关的并发症，直到获得一个足够简单的模型。 通过列举所有得到的近似，科学家有一个路线图，从复杂的，详细的表示物理系统，通过各种类型的近似和它们描述的系统行为。 换句话说，这个过程就像一座跨越尺度的数学桥梁：将微观机制与系统级现象联系起来。 在这个项目中，PI将把这些新的数学和相关的计算工具应用于三个目标应用领域：合金的晶体结构、发育途径的生化动力学和神经元网络。 通过更好地理解数学模型如何反映相关的物理细节，模型将更好地预测复杂系统的行为，并使复杂材料和工艺的设计和控制更加复杂。这项工作还包括一个教育组成部分，将制定跨学科科学的教学法。 该教学法将以系列研讨会的形式面向大学教师，以多学科的专题课程的形式面向大学生，以面向高中科学教师的教学研讨会和网络资源的形式面向高中教师。技术概要该职业奖支持以开发复杂系统的简单模型为目的的理论和计算研究。简单的数学模型在科学探究中一直扮演着重要的角色。 对于具有分离尺度或对称性的系统，有标准的技术可以从复杂的机械描述中构建简约的表示。 结合信息论、微分几何和拓扑学的最新进展，提出了推理复杂物理系统模型中机制和现象之间关系的新方法。 在这项研究中，PI采用的方法是，多参数模型的预测形成嵌入在抽象数据空间中的流形。 已经观察到，典型的模型流形是有界的，并且简单的近似模型驻留在模型流形的边界上。 流形边界近似法构造了一系列极限近似，即对应于一系列简单近似模型的“小参数”。 这项研究将把这些结果扩展到三个目标应用领域：合金晶体结构、发育途径的生化动力学和神经元网络。 在这些目标应用领域内，PI将开发一种新的建模方法，该方法基于“上确界模型”的概念，即同时解释多个行为的最小模型。 当与流形边界近似方法相结合时，上确界模型概念使人们能够从重叠系统组件的几个模型引导到更大系统的模型，直接探索这些模型在计算上具有挑战性或不可行。这项工作还包括一个教育部分，将发展跨学科科学的教学法。 该奖项将以系列研讨会的形式面向大学教师，以多部门、专题课程的形式面向大学生，以面向高中理科教师的教学研讨会和网络资源的形式面向高中教师。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Information geometry for multiparameter models: new perspectives on the origin of simplicity

• DOI: 10.1088/1361-6633/aca6f8
• 发表时间: 2021-11
• 期刊: Reports on Progress in Physics
• 影响因子: 18.1
• 作者: Katherine N. Quinn;Michael C. Abbott;M. Transtrum;B. Machta;J. Sethna

Piecemeal Reduction of Models of Large Networks

大型网络模型的逐步缩减

• DOI: 10.1109/cdc45484.2021.9683471
• 发表时间: 2021
• 期刊: Conference on Decision and Control
• 作者: Francis, Benjamin L.;Transtrum, Mark K.;Saric, Andrija T.;Stankovic, Aleksandar M.
• 通讯作者: Stankovic, Aleksandar M.

Selecting simple, transferable models with the supremum principle

• DOI: 10.1103/physrevresearch.4.l032044
• 发表时间: 2022-09
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Cody Petrie;Christian Anderson;Casie Maekawa;Travis Maekawa;M. Transtrum