Data-driven modelling and computation-improving the efficiency of computations in applied mathematics through scientific machine learning

数据驱动建模与计算——通过科学机器学习提高应用数学计算效率

• 批准号: 2777754
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2777754
• 关键词: Data; driven; modelling; computation; improving

Evidently, for many problems, computers have the capacity to outperform humans in classifications. I am intrigued by the contrary case where humans can predict or classify outcomes well and where computers might struggle. An obvious example of this comes in the form of image or text data. These types of problems are often trivial for humans to analyse but add a layer of complexity for a machine- how does one teach a machine to dissect a picture and analyse patterns? How does a machine learn to draw inference and emotion from a tweet consisting only of text? Perhaps what interests me most about these problems is the ability of a machine to learn an inherently human trait, albeit in a very different way- it so purely and explicitly imitates a learning process, relatable to that of a human. In talking with Professor David Large and Doctor Christopher Fallaize, I learned that classifying image data captured by satellites using InSAR could be crucial in determining the condition of peatlands across the world, without the need to conduct prior large scale field research. Management of peatlands is vital in carbon sequestration, as well as maintaining the diverse ecosystems that inhabit them, and disturbing them releases huge amounts of greenhouse gases into the atmosphere. In developing machine learning methods on these images, particularly through the use of time series, the conditions of peat can be classified by tracking their annual and sub-annual surface motion. These classifications could be used to identify areas of concern that could require restoration programmes. I am interested in the application of time series to image data, especially in the context of diagnosing the condition of peatlands. This work could prove vital in ensuring their conservation, which has been threatened by climate change.

显然，对于许多问题，计算机有能力在分类方面胜过人类。我对相反的情况很感兴趣，人类可以很好地预测或分类结果，而计算机可能会挣扎。一个明显的例子是图像或文本数据。这些类型的问题对于人类来说往往是微不足道的，但对于机器来说却增加了一层复杂性-如何教机器解剖图片并分析模式？机器如何学会从仅由文本组成的推文中得出推理和情感？也许这些问题最让我感兴趣的是机器学习人类固有特征的能力，尽管是以一种非常不同的方式-它如此纯粹和明确地模仿一个学习过程，与人类的学习过程有关。在与大卫大教授和克里斯托弗Fallaize博士交谈，我了解到，分类图像数据捕获的卫星使用干涉合成孔径雷达可能是至关重要的，在确定世界各地的泥炭地的条件，而不需要事先进行大规模的实地研究。泥炭地的管理对于碳固存至关重要，同时也维持着栖息在泥炭地上的多样化生态系统，扰乱泥炭地会向大气中释放大量的温室气体。在开发这些图像的机器学习方法时，特别是通过使用时间序列，可以通过跟踪其年度和次年度表面运动来对泥炭的状况进行分类。这些分类可用于确定可能需要恢复方案的关切领域。我对时间序列在图像数据中的应用感兴趣，特别是在诊断泥炭地状况的背景下。这项工作可能对确保受到气候变化威胁的保护至关重要。