The Synthesis of Mathematical and Data Driven Modelling of Complex Physical Systems

复杂物理系统数学和数据驱动建模的综合

• 批准号: 2130619
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2130619
• 关键词: Synthesis; Mathematical; Data; Driven; Modelling

Forecasting and prediction are operations of critical importance in the energy sector. Whether it is predicting the output of a solar or wind farm, forecasting the energy demand at city and regional levels, predicting the operational condition of assets such as gas turbine plants or the long term environmental impact of engineering operations, they all have to optimize under great uncertainty in a coherent and consistent manner. This proposed PhD posits that an overarching theoretical, methodological and practical framework to integrate both data driven and physics based models will provide greater modelling capability and representation as well as superior predictive capability in the presence of uncertainty.

预测和预报是能源部门至关重要的业务。无论是预测太阳能或风力发电场的输出，预测城市和区域层面的能源需求，预测燃气涡轮机厂等资产的运行状况，还是工程运营的长期环境影响，它们都必须在很大的不确定性下以连贯一致的方式进行优化。这一拟议的博士学位假定，一个总体的理论，方法和实践框架，以整合数据驱动和基于物理的模型将提供更大的建模能力和代表性，以及上级预测能力的不确定性的存在。