School of Science Department of Physics 75 Physics-Guided Data-Driven Modeling to Understand Complex Phenomena and to Solve Real-World Problems Supervisor: ZHANG Rui / PHYS Co-Supervisor: LI Sai Ping / PHYS Student: XIONG Jiujiu / PHYS Course: UROP 1000, Summer Understanding collective motion in active matter systems often begins with empirical observation. In this study, we start from desert locusts—classic self-propelled individuals displaying complex swarm behaviour— and use them as a biological inspiration to explore theoretical models of alignment. We first examine the Vicsek Model and its foundational assumptions, including the role of density and neighbourhood averaging, then extend our scope to updated variants incorporating scalar and vectorial noise, Binder cumulants, and bifurcation phenomena. Through virtual reality experiments and numerical simulations, we reveal that locust alignment depends more critically on the order parameter—the quality of visual social cues—than on density alone. In refining our models and simulation logic, we circle back to the locust system: incorporating perceptual bifurcation behaviour and stochastic leader-following. These findings bridge the gap between theoretical modelling and biological reality and suggest new strategies for decentralized control in robotics and swarm intelligence. Simulations of Active Viscoelastic Materials Supervisor: ZHANG Rui / PHYS Student: HU Yining / PHYS Course: UROP 1100, Summer Many soft materials including hydrogels are viscoelastic materials. These soft elastic materials are flexible and responsive and are promising for a range of applications. There is a new trend in constructing elastic or mechanical metamaterials to achieve material properties that do not exist in nature. In this project, we focus on using machine learning method to design novel elastic metamaterials. Initial work involved a comprehensive literature review and successful replication of an ML-driven metamaterial design project, acquiring core computational physics proficiencies. This foundational progress yielded a versatile computational engine and a clear strategic direction. Future work will adapt this framework to design and optimize metamaterials for diverse mechanical phenomena.
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