Bayes in space: adding uncertainty to deep learning in solar physics

太空中的贝叶斯：为太阳物理深度学习增加不确定性

• 批准号: 2743098
• 金额: --
• 依托单位: Northumbria University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2743098
• 关键词: Bayes; space; adding; uncertainty; deep

Deep learning is an increasingly common tool used in astrophysics, with the flexibility and speed of neural networks (NN) meaning they can be applied to a wide range of data intensive problems. For example, NNs have been developed to provide rapid estimates of the Sun's atmospheric temperatures, helping to address major questions in solar physics, e.g., how is the corona heated to temperatures of over a million degrees? One key issue is that the current networks do not provide a measure of the result's uncertainty, which is crucial for determining whether the answers provided by the NN are trustworthy. This is also of great importance in an industry setting, where a poor or incorrect answer could be dangerous or expensive.During this project, the student's goal is to develop NNs that incorporate uncertainty through the implementation of Bayesian approaches, which are currently at the forefront of deep learning research. The student would learn how to programme in Python and develop knowledge of the cutting-edge deep learning software (Tensorflow, Pytorch). Further, they would gain experience in the field of solar physics, working with data from NASA/ESA satellites. The developed networks will be applied to problems important in solar physics research (e.g., solar plasma temperature estimation, removal of data noise/corruption), with the opportunity to share work at scientific conferences in the UK and abroad. The student will also apply the skills learnt in an industry setting, undertaking a placement with our industrial partners.

深度学习是天体物理学中越来越常用的工具，神经网络 (NN) 的灵活性和速度意味着它们可以应用于广泛的数据密集型问题。例如，神经网络的开发是为了快速估计太阳的大气温度，有助于解决太阳物理学中的主要问题，例如日冕是如何加热到超过一百万度的温度的？一个关键问题是，当前的网络无法提供结果不确定性的衡量标准，而这对于确定神经网络提供的答案是否可信至关重要。这在行业环境中也非常重要，因为糟糕或不正确的答案可能是危险或昂贵的。在这个项目中，学生的目标是通过实施贝叶斯方法来开发包含不确定性的神经网络，贝叶斯方法目前处于深度学习研究的前沿。学生将学习如何使用 Python 进行编程，并掌握尖端深度学习软件（Tensorflow、Pytorch）的知识。此外，他们还将利用美国宇航局/欧洲航天局卫星的数据获得太阳物理学领域的经验。开发的网络将应用于太阳物理研究中的重要问题（例如，太阳等离子体温度估计、消除数据噪声/损坏），并有机会在英国和国外的科学会议上分享工作成果。学生还将运用在行业环境中学到的技能，在我们的行业合作伙伴中进行实习。