Analysing the Motion of Biological Swimmers

考虑一个在某种介质中运动的可变形物体。一般来说，人们不能仅仅根据物体的表面形状来推断物体的内部变形：它需要详细了解物体每一点的机械特性和受力情况。然而，这类逆问题在软物质物理、材料科学和工程中具有相当大的实用性和基础性。我们认为秀丽隐杆线虫的相对简单和受限的几何形状，以及我们对其解剖学和材料特性的广泛了解，使反问题易于处理。深度学习为解决反问题提供了一种新的、有前途的方法，它已经改变了人工智能在标准任务上的表现，特别是在语言和视觉领域，现在越来越多地应用于机器人领域。这项拟议的研究旨在探索利用深度学习来了解秀丽隐杆线虫在三维复杂流体中游泳的视频片段中作用于其身体的内力和外力的可行性。

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.