Nonlinear Partial Differential Equations and Applications

非线性偏微分方程及其应用

• 批准号: 1600129
• 负责人: Panagiotis Souganidis
• 金额: $ 26万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1600129&HistoricalAwards=false
• 关键词: Nonlinear; Partial; Differential; Equations; Applications

The modeling of many phenomena in the physical and social sciences and engineering, such as porous media, composite materials, turbulence and combustion, traffic models, spread of crime, agent models and others, involve heterogeneous media described by partial differential equations. These typically depend upon many parameters and vary randomly on a small scale. In addition, often the available information (e.g., data used in weather prediction) is not exact (deterministic) but statistical (random), with large fluctuations. On macroscopic scales that are much larger than the ones of the heterogeneities, the models often exhibit an effective deterministic behavior, which is much simpler than the original one. The process of averaging such data is known as homogenization. Mathematically, this means that the original random problem is replaced by a deterministic one. When this averaging is not possible, which is typically the case when the fluctuations are too strong (wild), it is necessary to deal with so-called stochastic media (stochastic partial differential equations), which have rather singular behavior in space and time. The mathematical study of both the stochastic averaging and the stochastic partial differential equations requires original ideas and the development of new methodologies, since both topics fall outside the traditional theories of averaging and partial differential equations. Another burgeoning area of research in which similar issues surface is mathematical biology, where experiments at the molecular scale, as well as theoretical advances, have led to new, sophisticated mathematical models. Novel tools and ideas are needed to study these problems further and to validate all the relevant regimes/scales of the parameters affecting the experimentally observed and theoretically conjectured behaviors. This project is directed at the development of general methodologies to study random homogenization, nonlinear stochastic partial differential equations, and applications to front propagation, phase transitions, and mathematical biology. Random environments are much more general than periodic ones. The latter basically involve fixed translations of a certain equation, whereas the former can be thought of as involving all possible (relevant) equations. This leads to considerable issues concerning the lack of compactness. It is therefore necessary to develop novel arguments that combine both the differential and random structures of the media under scrutiny. In this setting, the equation is the random variable and the special dependence signifies the location in space where the equation is observed. The principal investigator and his collaborators were the first to consider stochastic homogenization in stationary ergodic environments. A large part of the project is dedicated to further development of the theory. Stochastic partial differential equations have coefficients with very singular (Brownian) behavior. In the linear context, this can usually be handled by known methods, such as the classical martingale approach. This method is based on the linear character of the higher order part of the equation and thus cannot be used for nonlinear problems, where it is necessary to find appropriate alternative notions of solutions. These, in the context of first- and second-order nonlinear equations, are the stochastic viscosity and pathwise entropy solutions that have been introduced by the principal investigator and his collaborators. A part of the project is the study of the qualitative behavior/properties of these solutions. In the context of mathematical biology, the principal investigator plans to work on models of adaptation/selection as well as on models of the biology of development. The former concerns questions related to the adaptation of species to global change, the resistance of insects to pesticides, etc. The latter aims at developing models to study how positional information is provided to proliferating cells, the main questions being the formation and location of sharp and precise boundaries.

在物理和社会科学和工程中的许多现象的建模，例如多孔媒体，复合材料，湍流和燃烧，交通模型，犯罪传播，代理模型等，涉及部分微分方程描述的异构媒体。这些通常取决于许多参数，并且在小规模上随机变化。此外，通常不准确（确定性）的可用信息（例如，在天气预测中使用的数据），而是统计（随机），并且波动很大。在宏观尺度上比异质性大得多，模型通常表现出有效的确定性行为，这比原始的行为要简单得多。平均此类数据的过程称为均匀化。从数学上讲，这意味着原始的随机问题被确定性的问题取代。当这种平均是不可能的（通常是波动太强（野性）时）时，有必要处理所谓的随机介质（随机偏微分方程），这在时空和时间上具有相当奇异的行为。 对随机平均和随机偏微分方程的数学研究都需要原始思想和新方法的发展，因为这两个主题都符合传统的平均和部分微分方程的理论。 一个迅速发展的研究领域是，类似问题表面是数学生物学，在该研究中，分子量表的实验以及理论上的进步导致了新的，复杂的数学模型。需要新的工具和思想来进一步研究这些问题，并验证影响实验观察到的参数和理论上猜想的行为的所有相关制度/量表。该项目致力于开发一般方法，以研究随机均质化，非线性随机偏微分方程以及对前传播，相变和数学生物学的应用。随机环境比周期性环境更一般。后者基本上涉及特定方程式的固定翻译，而前者可以被认为是涉及所有可能（相关）方程式。这导致有关缺乏紧凑性的大量问题。因此，有必要开发新的论点，以结合媒体的差异和随机结构的审查。在这种情况下，方程是随机变量，特殊依赖性表示观察到方程的空间位置。首席研究员及其合作者是第一个考虑在固定的厄贡环境中随机均质化的人。该项目的很大一部分致力于进一步发展该理论。 随机部分微分方程具有非常单数（布朗）行为的系数。在线性上下文中，通常可以通过已知方法来处理，例如经典的Martingale方法。该方法基于方程式高阶的线性特征，因此不能用于非线性问题，在此需要找到适当的解决方案概念。这些在一阶和二阶非线性方程的背景下是主要研究者及其合作者引入的随机粘度和路径熵解决方案。 该项目的一部分是研究这些解决方案的定性行为/特性。在数学生物学的背景下，主要研究者计划致力于适应/选择模型以及发展生物学模型。前者涉及与物种适应全球变化，昆虫对农药的抗性有关的问题。后者旨在开发模型来研究如何提供位置信息以增殖细胞，主要问题是形成和锐利边界的形成和位置。