Integrating remote sensing, machine learning, and advanced ocean modeling for the improved prediction of coastal change

这项研究的重点是利用计算技术来更好地了解沿海环境。这个项目涵盖的范围很广，因此可以灵活地针对我的研究。指定先前技术的一些原因是由于使用现实世界数据（如遥感图像）通过机器学习技术推动模型改进的可能性。在我的博士学位中，目前正在进行的一些重点项目是开发覆盖查戈斯群岛的改进的水深数据，并将其应用于了解该地区的流体流动，从而支持进一步研究侵蚀模式、物质/营养物质流动等。正在探索的另一个主要概念侧重于使用物理驱动的机器学习算法，以允许对局部条件下变化/扰动的昂贵流模型进行廉价/快速预测。

The research focuses on the use of computational techniques to develop a better understanding of the coastal environment. This project covers a wide scope and thus allows for flexibility in where to target my research on. Some of the reasons why the prior techniques are specified are due to the possibility of using real-world data such as remote sensing imagery to drive improvements in our models through machine learning techniques.Some of the current ongoing key projects within my PhD are developing improved bathymetric data covering the Chagos Archipelago and its application for understanding fluid flow through the region which can, in turn, support further research in understanding erosion patterns, the flow of materials/nutrients, etc. Another major concept being explored focuses on the usage of physics-driven machine learning algorithms to allow for a cheap/fast prediction of an expensive flow model for changes/perturbations in the local conditions.