CDS&E/Collaborative Research: A New Framework for Computational Model Validation

CDS

• 批准号: 1663667
• 负责人: Erik Johnson
• 金额: $ 38.71万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1663667&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; New; Framework

Simulations play a key role in helping decision makers respond to complex societal problems, ranging from strategies for enhancing infrastructure resilience to natural hazards, to understanding financial markets, or managing air traffic. However, to be effective, individuals must select appropriate models and understand their limitations. This project conducts fundamental research to create a computationally efficient paradigm for model validation designed to guide decisions on model accuracy and validity. The resulting probabilistic framework will allow users to explore many families of models and to choose the ones that are the most useful. Advanced computational algorithms and architectures will enable the framework to leverage vast amounts of data from devices, platforms, sensors and systems and users to simultaneously evaluate multiple models. By developing an integrated framework of model falsification and Bayesian model selection, the project investigators give researchers and decision makers a powerful tool for effectively using models to understand and respond to emerging societal challenges and opportunities.Integrating Bayesian model selection with two model falsification phases forms a novel probabilistic framework for model validation: initial falsification eliminates unsuitable models that do not fit measured data, while final falsification selects the model classes with the most accurate simulations. New approaches to model falsification -- false discovery rate testing and model likelihood thresholds -- provide a cohesive framework that accommodates many types of dynamic response data. The approach is optimized and calibrated with testbed modeling problems such as NASA's benchmark for turbulence model validation and modeling a fully instrumented base isolated building. This work will transform the efficiency, accuracy and widespread applicability of model validation, allowing it to be used for previously intractable science and engineering applications. The researchers will explore modeling collaborations in promising areas including turbulence, material behavior, and biochemical reactions. The project will enhance awareness of model validation through education and outreach activities. Research results will be disseminated in journal publications and presentations at national and international conferences. Two graduate students will be mentored and research concepts and results will be incorporated into two graduate uncertainty quantification courses and an undergraduate engineering risk analysis class. The researchers will also incorporate research findings into their ongoing K-12 outreach efforts, including a model-building module targeting middle/high school STEM enrichment programs.

模拟在帮助决策者应对复杂的社会问题方面起着关键作用，从增强基础设施弹性到自然危害的策略到了解金融市场或管理空中流量。 但是，为了有效，个人必须选择适当的模型并了解其局限性。 该项目进行了基本研究，以创建一个计算高效的模型验证范式，旨在指导模型准确性和有效性的决策。 由此产生的概率框架将使用户能够探索许多模型家庭，并选择最有用的模型。 高级计算算法和体系结构将使该框架能够利用设备，平台，传感器和系统以及用户的大量数据，以同时评估多个模型。 通过开发模型伪造和贝叶斯模型选择的综合框架，项目研究人员为研究人员和决策者提供了一种有效使用模型来理解和响应新出现的社会挑战和机会的强大工具模拟。 建模伪造的新方法 - 错误的发现率测试和模型可能性阈值 - 提供了一个凝聚力的框架，可容纳许多类型的动态响应数据。 该方法通过测试台建模问题进行了优化和校准，例如NASA的基准湍流模型验证和建模完全仪器的基础隔离建筑物。 这项工作将改变模型验证的效率，准确性和广泛适用性，从而使其用于以前棘手的科学和工程应用。 研究人员将探索在有前途的领域的建模合作，包括湍流，材料行为和生化反应。 该项目将通过教育和外展活动提高对模型验证的认识。研究结果将在国民和国际会议的期刊出版物和演讲中传播。将指导两名研究生，研究概念，结果将被纳入两个研究生不确定性量化课程和一个本科工程风险分析课程中。 研究人员还将将研究结果纳入其正在进行的K-12外展工作中，包括针对中/高中STEM富集计划的模型建设模块。

项目成果

Investigation of Model Falsification Using Error and Likelihood Bounds with Application to a Structural System

使用误差和似然界研究模型证伪及其在结构系统中的应用

• DOI: 10.1061/(asce)em.1943-7889.0001440
• 发表时间: 2018
• 期刊: Journal of Engineering Mechanics
• 影响因子: 3.3
• 作者: De, Subhayan;Brewick, Patrick T.;Johnson, Erik A.;Wojtkiewicz, Steven F.
• 通讯作者: Wojtkiewicz, Steven F.

A hybrid probabilistic framework for model validation with application to structural dynamics modeling

• DOI: 10.1016/j.ymssp.2018.10.014
• 发表时间: 2019-04
• 期刊: Mechanical Systems and Signal Processing
• 影响因子: 8.4
• 作者: Subhayan De;P. Brewick;Erik A. Johnson;S. Wojtkiewicz