CDI Type II Proposal -- From Models and Data to Knowledge and Understanding

CDI II 类提案——从模型和数据到知识和理解

• 批准号: 0941373
• 负责人: Scott Pratt
• 金额: $ 180万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941373&HistoricalAwards=false
• 关键词: CDI; Type; II; Proposal; Models

Two central questions facing numerous fields will be addressed by this award:  "How does one reach rigorous quantitative conclusions when the science involves complicated numerical models and large scale data sets?" and "How can one use computational methods to point out new insights into what are the most important features, parameters and data?". To this end, a generalized statistical and visualization structure will be developed for the purpose of comparing and analyzing large-scale computational models to large heterogenous data sets. The codes, techniques, algorithms and methodologies applied to this project will be applied to critical problems in four different fields:  high-energy nuclear physics, astrophysics, meteorology and biology. By devising a flexible open-source framework, the products of this award will be extendable to numerous other problems in the natural or social sciences involving sophisticated simulations and complicated data structures.The way in which simulations and data are analyzed will be revolutionized by the capabilities developed by this work. Fundamental model parameters related to each of the four problems, such as the percentage of the dark matter in the universe or the viscosity of the quark-gluon plasma, will be stated with rigorous uncertainties. Methods will be developed and implemented for quantitatively stating which data were most important for constraining parameters or validating model assumptions. Furthermore, these same methodologies will be applied to assess the value of additional data, making it possible to quantitatively assess the potential of experimental upgrades or additional statistics. Students and postdocs will be trained in an emerging science centered around the analysis of complex systems. In addition to their own scientific discipline, they will be exposed to 21st century tools in statistics, computation and visualization.

该奖项将解决众多领域面临的两个核心问题：“当科学涉及复杂的数值模型和大规模数据集时，如何得出严格的定量结论？以及“如何使用计算方法来指出最重要的特征、参数和数据的新见解？".为此，将开发一个通用的统计和可视化结构，以便将大规模计算模型与大型异质数据集进行比较和分析。应用于该项目的代码、技术、算法和方法将应用于四个不同领域的关键问题：高能核物理、天体物理、气象学和生物学。通过设计一个灵活的开源框架，该奖项的产品将可扩展到自然科学或社会科学中涉及复杂模拟和复杂数据结构的许多其他问题。通过这项工作开发的功能，模拟和数据分析的方式将发生革命性变化。与这四个问题有关的基本模型参数，如宇宙中暗物质的百分比或夸克-胶子等离子体的粘度，将以严格的不确定性来说明，将制定和实施方法，以定量说明哪些数据对约束参数或验证模型假设最重要。此外，这些相同的方法将用于评估额外数据的价值，从而能够定量评估实验升级或额外统计数据的潜力。学生和博士后将接受以复杂系统分析为中心的新兴科学培训。除了他们自己的科学学科，他们将接触到21世纪的统计，计算和可视化工具。