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.

预测和预测是能源部门至关重要的运作。无论是预测太阳能还是风电场的产出，预测城市和区域级别的能源需求，预测诸如燃气轮机植物等资产的运营条件，还是工程运营的长期环境影响，它们都必须在下面进行优化以连贯和一致的方式进行巨大的不确定性。该拟议的博士学位认为，在不确定性存在下，基于数据驱动和物理学的模型都集成了基于数据驱动和物理的模型的总体理论，方法论和实用框架将提供更大的建模能力和表示能力，以及在不确定性的存在下提供卓越的预测能力。