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.

预测高维复杂多尺度系统的未来状态是我们在从气候科学到流行病学等领域面临的挑战。即使基本的物理机制已经确定，实际的演化方程往往是未知的。该项目将开发一个计算成本低的机器学习框架用于预测。建议的数学框架提供了一个预测连同其不确定性的量化。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的亚网格尺度参数化的常年问题，这是当前气候模型中缺少的关键要素。