Analysing the Motion of Biological Swimmers

分析生物游泳者的运动

• 批准号: EP/S01540X/1
• 负责人: David Hogg
• 金额: $ 31.45万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS01540X%2F1
• 关键词: Analysing; Motion; Biological; Swimmers; Analysing; Motion; Biological; Swimmers

Consider a deformable object moving in some medium. In general, one cannot infer the internal deformation of the body based on surface shape alone: it requires detailed knowledge of the mechanical properties and forces operating at every point of the object. And yet, such inverse problems are of considerable practical and fundamental interest in soft matter physics, material science and engineering. We propose that the relatively simple and constrained geometry of the worm C. elegans, and our extensive knowledge of its anatomy and material properties makes the inverse problem tractable. A new and promising approach to the solution of inverse problems is provided by deep learning, which has already transformed performance on standard tasks from across artificial intelligence, particularly in the areas of language and vision, and increasingly now in robotics. The proposed research is to explore the feasibility of using deep learning to understand the internal and external forces acting on the body of C. elegans from video footage of the worm swimming in three dimensional complex fluids.

考虑一个在某些介质中移动的可变形物体。通常，仅基于表面形状来推断身体的内部变形：它需要详细了解物体每个点运行的机械性能和力。然而，这种反问题对软物质物理，材料科学和工程的实用和基本兴趣具有相当大的实际兴趣。我们建议，蠕虫秀丽隐杆线虫的相对简单和约束的几何形状，以及我们对其解剖结构和材料特性的广泛了解使逆问题可解决。深度学习提供了一种新的且有前途的方法来解决反问题的方法，这已经改变了整个人工智能的标准任务的绩效，尤其是在语言和愿景领域，现在越来越多地在机器人技术中。拟议的研究是探索利用深度学习来理解从三维复杂液中蠕虫游泳的视频录像中对秀丽隐杆线虫体内作用的内部力量和外部力量的可行性。