Collaborative Research: MSPA-CSE: Equation-Free Modeling of Biological Self Organization: Coarse Computational Swarming

合作研究：MSPA-CSE：生物自组织的无方程建模：粗计算集群

• 批准号: 0434328
• 负责人: Jeffrey Moehlis
• 金额: $ 23万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0434328&HistoricalAwards=false
• 关键词: Collaborative; Research; MSPA; CSE; Equation

Animal populations, such as fish and zooplankton in the oceans, and terrestrial species from bison to locusts and midges, can self-organize into schools and swarms through the interactions of individuals with each other and their environment. This project aspires to achieve a new level of understanding of the emergence of such population-level behaviors through the development, implementation, and validation of coarse-grained, systems-level numerical methods for individual-based models of collections of organisms. Systems-level tasks enabled through this computational approach include long-time predictions, stability and bifurcation analysis (which details how observed behavior depends on environmental and organism properties), control, and optimization.Understanding aggregation behavior is of both theoretical and applied importance, for problems as diverse as fisheries management and pest control. The details of animal behavior involve individual-level dynamics that are too complicated to analyze rigorously. What is of interest, however, are the macroscopic dynamics of populations. This project will build a tool for providing this understanding, in a systematic, flexible, and effective way. The algorithms and ideas that will be developed will also have an impact outside of biology, with potential applications in computational chemistry, materials science, and economics.

动物种群，如海洋中的鱼类和浮游动物，以及从野牛到蝗虫和蠓的陆地物种，可以通过个体之间的相互作用及其环境的相互作用，自我组织成鱼群和蜂群。该项目旨在通过开发，实施和验证粗粒度，系统级的数值方法，以个人为基础的生物体集合模型的出现，实现对这种群体级行为的新的理解水平。通过这种计算方法实现的系统级任务包括长期预测，稳定性和分叉分析（详细说明观察到的行为如何取决于环境和生物体属性），控制和优化。了解聚合行为在理论和应用上都很重要，对于渔业管理和害虫控制等各种问题都很重要。动物行为的细节涉及个体层面的动态，这些动态太复杂而无法严格分析。然而，令人感兴趣的是种群的宏观动态。 该项目将建立一个工具，以系统，灵活和有效的方式提供这种理解。将开发的算法和想法也将在生物学之外产生影响，在计算化学，材料科学和经济学中具有潜在的应用。