Interface-aware numerical methods for stochastic inverse problems

随机反问题的接口感知数值方法

• 批准号: DP210103092
• 负责人: Prof Santiago Badia
• 金额: $ 33.35万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-10-23 至 2024-10-22
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210103092
• 关键词: Interface; aware; numerical; methods; stochastic

This project aims to design novel high-performance numerical tools for solving large-scale forward and inverse problems dominated by stochastic interfaces and quantifying associated uncertainties. In real-world applications such as groundwater, these tools are instrumental for assimilating big datasets into mathematical models for providing reliable predictions. By advancing and integrating high-order polytopal schemes, multilevel methods, transport maps, and dimension reduction, this project's anticipated outcomes are highly accurate and cost-efficient numerical schemes, certified by rigorous mathematical analysis. This should provide data-centric simulation tools with enhanced reliability, for engineering and scientific applications.

该项目旨在设计新颖的高性能数值工具，用于解决由随机接口主导的大规模正向和逆向问题并量化相关的不确定性。在地下水等现实应用中，这些工具有助于将大数据集吸收到数学模型中，以提供可靠的预测。通过推进和集成高阶多面方案、多级方法、传输图和降维，该项目的预期结果是高度准确且具有成本效益的数值方案，并经过严格的数学分析认证。这将为工程和科学应用提供具有增强可靠性的以数据为中心的仿真工具。