Machine learning models to extract accurate material transport

机器学习模型可提取准确的材料传输

• 批准号: 2887164
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887164
• 关键词: Machine; learning; models; extract; accurate

The student will develop machine learning models to extract accurate material transport and strength coefficients from time-series data, obtained from high-fidelity simulations as well as experiments with high-power lasers. These will be used to improve our understanding of materials under extreme conditions, particularly in presence of non-local processes involving turbulent and magnetized plasmas. The goal of the project is to develop a novel graph neural network framework to address the complex micro-physics of material properties and to identify their emergent behaviour via closed mathematical expressions using symbolic regression techniques. This has practical applications ranging from the design of spacecraft components to the modelling of the energy balance in cluster of galaxies.

学生将开发机器学习模型，以从高保真模拟以及具有高功率激光器的实验中获得准确的材料传输和强度系数。这些将用于提高我们对极端条件下材料的理解，特别是在存在涉及湍流和磁化等离子体的非本地过程的情况下。该项目的目的是开发一个新型的图形神经网络框架，以解决材料特性的复杂微物理，并使用符号回归技术通过封闭的数学表达式来识别其新兴行为。这具有从航天器组件的设计到星系群中能量平衡的建模，具有实际应用。