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

动物种群，如海洋中的鱼和浮游动物，以及从野牛到蝗虫和蚊子的陆地物种，可以通过个人与彼此及其环境的互动，自组织成群体和群体。该项目致力于通过开发、实施和验证基于个体的生物体集合模型的粗粒度、系统级数值方法，达到对这种种群水平行为出现的新水平的理解。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依赖于环境和生物属性)、控制和优化。对于渔业管理和病虫害控制等各种问题，理解聚集行为具有理论和应用两方面的重要性。动物行为的细节涉及个体层面的动态，太复杂了，无法进行严格的分析。然而，令人感兴趣的是人口的宏观动态。这个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。即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、材料科学和经济学方面具有潜在的应用。

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.