加拿大女王大学(Queen’s University)机械与材料工程(Mechanical and Material Engineering)范迪夏课题组诚招博士研究生,联合培养博士生,以及访问学者等若干名加入团队。团队致力于智能流体力学,仿生漩涡流体的控制与感知,新型空海变形结构与材料等研究,用于海洋工程结构健康监测,仿生两栖(空海)机器人群的设计与控制。更多学术信息,请参考个人主页:https://me.queensu.ca/People/Fan/ ,与实验室i4-FSI Lab(Intelligent,informational,integrative,interdisciplinary Fluid-Structure Interaction)课题组的主页:https://www.i4fsi.com/
Dixia Fan obtained his Ph.D. (2019) and MSc (2016) from MIT Mechanical Engineering and BSc (2013) from Shanghai Jiao Tong University Naval Architecture, Ocean and Civil Engineering. He is an assistant professor (2021) at Queens University, Canada, the director of the i4-FSI Lab and a faculty member of Ingenuity Labs (https://ingenuitylabs.queensu.ca/). Additionally, He is the founder of the MIT Smart Hydrodynamics Lab, featuring the world's first intelligent towing tank (ITT). His research interests focus on physics-informed (and -informative) machine learning, and vortical flow control and sensing in both fundamental fluid-structure interaction (FSI) problems as well as aero/aquatic bio-propulsion and maneuvering
【团队介绍】
The i4-FSI Lab (Intelligent, Informational, Integrative, and Interdisciplinary Fluid-Structure Interaction Lab) is at the intersection of fluid mechanics, artificial intelligence, and nature-inspired design. We are interested in combining domain expertise (fluid mechanics, robotics, and control) and proper machine learning tools to address the inherent spatial and temporal non-linearity and multiscality of fluid-related problems at a greater scale and a broader scope.
We envision a research paradigm shift in fluid mechanics to a physics-informed (and -informative) probabilistic learning framework, which leads to disruptive technology transform in the aerospace and marine industry to a more efficient, safe, and eco-friendly future. Some of the current research topics include 1) study of aerodynamics and hydrodynamics of fish, birds, insects, mammals locomotion, 2) development of novel programmable metamaterial for aero/hydro morphing structures and 3) advancement of machine learning and bio-inspired algorithm for vortical flow control and sensing.