Statistical Methods in Computational Modeling and Virtual Experiments

计算建模和虚拟实验中的统计方法

• 批准号: 9714380
• 负责人: John Tucker
• 金额: $ 2.2万
• 依托单位: National Academy of Sciences
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-06-01 至 2000-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9714380&HistoricalAwards=false
• 关键词: Statistical; Methods; Computational; Modeling; Virtual

PI: John Tucker Proposal Number: DMS 9714380 Project: Statistical Methods in Computational Modeling and Virtual Experiments Abstract: The Committee on Applied and Theoretical Statistics (CATS) will convene a multidisciplinary study panel on statistical methods in computational modeling and virtual experiments. The panel will recommend sound methods for identifying and quantifying uncertainties that arise in simulation experiments. The panel will develop recommendations for improved and, where necessary, new statistical methods and data analysis techniques for use in simulation modeling and virtual experiments. It will also recommend appropriate statistical methods to assist practitioners in their use of experimental data when validating simulation models or assessing model fit and predictive performance. These recommendations will address modeling and simulation limitations associated with ever increasingly complex systems. The panel's report will provide examples to illustrate the complexity of evaluating model combinations that arise from varying assumptions, parameters, and initial conditions within the models, will point out possible approaches for addressing those complexities from existing experimental design techniques, and will highlight areas needing further research. Experts may be consulted from the communities concerned with computer simulation, discrete events systems, and dynamic systems modeling, and any other scientists, researchers, or engineers with significant technology or methodology to contribute. A National Research Council panel of experts will examine the state-of-the-art for statistical methods used in computer simulation and modeling, and computational experimentation. This is needed because of an ever-growing dependence upon these kinds of simulations and "virtual" experiments in designing and evaluating such complicated devices and systems as semiconductors, automobiles, aircraft, spacecraft, or in very complex application areas such as transportation networks, government intelligence, and battlefield scenarios. The study will address how modeling and simulation tools can be developed with the robustness and reliability to handle the demands of emerging intelligence, domestic, and military technology. It will also look at limitations on modeling and simulation with respect to these increasingly complex systems.

PI：John Tucker提案编号：DMS 9714380项目：计算建模和虚拟实验中的统计方法摘要：应用和理论统计委员会(CATS)将召开一个关于计算建模和虚拟实验中统计方法的多学科研究小组。该小组将推荐可靠的方法来识别和量化模拟实验中出现的不确定性。该小组将提出改进的建议，并在必要时提出新的统计方法和数据分析技术，用于模拟建模和虚拟实验。它还将推荐适当的统计方法，以帮助从业者在验证模拟模型或评估模型拟合和预测性能时使用实验数据。这些建议将解决与日益复杂的系统相关的建模和仿真限制。专家小组的报告将举例说明评估模型组合的复杂性，这些模型组合因模型内不同的假设、参数和初始条件而产生，将指出从现有实验设计技术解决这些复杂性的可能方法，并将突出需要进一步研究的领域。可以咨询与计算机仿真、离散事件系统和动态系统建模相关的社区的专家，以及任何其他具有重要技术或方法论的科学家、研究人员或工程师。国家研究委员会的一个专家小组将审查计算机模拟和建模以及计算实验中使用的统计方法的最新水平。这是必要的，因为在设计和评估半导体、汽车、飞机、航天器等复杂设备和系统时，或在交通网络、政府情报和战场场景等非常复杂的应用领域，对这些类型的模拟和“虚拟”实验的依赖程度越来越高。这项研究将解决如何开发具有健壮性和可靠性的建模和仿真工具，以满足新兴的情报、国内和军事技术的需求。它还将研究与这些日益复杂的系统有关的建模和仿真方面的限制。