School of Science Department of Physics 69 Algorithm-augmented Ultrafast Spectroscopy Supervisor: ZHANG, Jingdi / PHYS Student: PENG, Huaiyue / PHYS Course: UROP1000, Summer Ultrafast laser pulses have emerged as an advantageous tool to uncover complex interaction and unlock novel functionalities in quantum materials. This is largely made possible by the superb temporal resolution of modern femtosecond lasers for triggering novel non-equilibrium phase transitions. Despite the success in capturing post-laser-excitation dynamics, it remains a challenge to unveil the radically faster rising dynamics from equilibrium to non-equilibrium state, which bears essential microscopic mechanism enabling the photo-induced phase transition pathway. The resolution to the challenge requires methodology for spectroscopic analysis with hyper resolution, that is, a temporal resolution beyond the “shutter” speed of the instrument. In this project, we explore the possibility to acquire such resolution by algorithm-augmented technique. 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: CHENG, Chun Hei / PHYS Course: UROP1100, Spring UROP2100, Summer Resistor networks are studied using a network science approach. We studied the change of voltages at each edge in square, hexagonal and scale-free resistor networks after removing resistors. A deep neural network is used to predict removed resistor in a resistor network with the voltage changes from a few nodes as input data. 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: HUANG, Hao / SENG Course: UROP1000, Summer This report replicates and extends a study on dynamic volatility spillover relationships between the Chinese carbon market and global energy markets during extreme climate shocks. Utilizing recent data, I validate and investigate the original findings, employing advanced econometric techniques to analyze volatility spillovers and estimate time-varying conditional correlations. By assessing the robustness of the results and exploring potential asymmetries and non-linearities, this study enhances our understanding of the complex dynamics between carbon and energy markets amidst climate shocks. Insights gained will aid policymakers, investors, and market participants in formulating effective risk management strategies and policy interventions.
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