Efficient multifidelity data-informed models for urban air quality

城市空气质量的高效多保真数据模型

• 批准号: 2446584
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2446584
• 关键词: Efficient; multifidelity; data; informed; models

The student will be part of a project which addresses the challenge of modelling one of the most important environmental problems currently affecting people's health, urban air pollution. Although it is well established that urban air quality can be modelled mathematically using partial differential equations, the inclusion of uncertainty propagation for this class of models requires multiple model evaluations with many different inputs, leading to excessive computational demands if only a high-fidelity model is used. There is therefore a pressing need for models which combine such techniques with reduced order approaches and parameter estimation informed by observational data sets. The aim of this project is to develop such multifidelity methods to accelerate the solution of uncertainty propagation by combining techniques from mathematical modelling, statistics, linear algebra and data science. The project will place the student at the forefront of research in numerical methods, and provide an excellent opportunity to develop skills working at the interface between applied mathematics, engineering and industry.

学生将参与一个项目，该项目解决了目前影响人们健康的最重要的环境问题之一城市空气污染的建模挑战。虽然它是公认的，城市空气质量可以用偏微分方程数学建模，包括这类模型的不确定性传播需要多个模型评估与许多不同的输入，导致过多的计算需求，如果只使用高保真模型。因此，迫切需要的模式，联合收割机这种技术与降阶的方法和参数估计的观测数据集。该项目的目的是开发这种多保真度方法，通过结合数学建模、统计学、线性代数和数据科学等技术，加速解决不确定性传播问题。该项目将使学生处于数值方法研究的最前沿，并提供了一个很好的机会来发展应用数学，工程和工业之间的接口工作的技能。