Self-adaptive and machine learning methods for two-dimensional stochastically perturbed shallow water equations

二维随机扰动浅水方程的自适应和机器学习方法

• 批准号: 2732303
• 金额: --
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2732303
• 关键词: Self; adaptive; machine; learning; methods

The project focusses on computer simulation of flood phenomena, and the influence of rain patterns, including climate change induced variability, which play a crucial role in the economy. The risk-assessment can help save lives and alleviate costs to citizens, businesses and governments. The complexity of flood phenomena requires simulations and statistical solutions for accurate estimation of flood risk.Ambiental Technical Solutions Ltd, a business specialised to flood-risk assessment, numerical simulation to provide insight to real-life situations for customers

该项目的重点是洪水现象的计算机模拟以及降雨模式的影响，包括气候变化引起的变化，这些在经济中发挥着至关重要的作用。风险评估有助于拯救生命并减轻公民、企业和政府的成本。洪水现象的复杂性需要模拟和统计解决方案来准确估计洪水风险。Ambiental Technical Solutions Ltd，一家专门从事洪水风险评估、数值模拟的企业，为客户提供对现实生活情况的洞察