A dynamical systems theory approach to machine learning

机器学习的动力系统理论方法

• 批准号: DP220100931
• 负责人: Prof Georg Gottwald
• 金额: $ 25.3万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220100931
• 关键词: dynamical; systems; theory; approach; machine

Forecasting the future state of a high-dimensional complex multi-scale system is a challenge we face in areas ranging from climate science to epidemiology. Even when basic physical mechanisms have been identified, the actual evolution equations are often unknown. This project will develop a computationally cheap machine learning framework for forecasting. The proposed mathematical framework provides a forecast together with a quantification of its uncertainty. We will develop sophisticated mathematical theory underpinning the novel methodology, as well as applying it to the perennial problem of subgrid-scale parametrisation of tropical convection, a missing key element in current climate models.

从气候科学到流行病学，预测高维复杂多尺度系统的未来状态是我们面临的一个挑战。即使已经确定了基本的物理机制，实际的演化方程也常常是未知的。这个项目将开发一个计算成本低廉的预测机器学习框架。所提出的数学框架提供了一种预测及其不确定性的量化。我们将开发支持新方法的复杂数学理论，并将其应用于热带对流亚网格尺度参数化的长期问题，这是当前气候模型中缺失的关键因素。