Zhanerke ALzhanova

That’s a really exciting topic — studying fluctuations in high-energy nuclear collisions gives deep insight into how quark–gluon plasma forms and evolves. 🔥 Working with C++ simulations is a great choice since it allows precise control over event generators and transport models. Many researchers use frameworks like UrQMD, AMPT, or hydrodynamic codes to model these extreme states of matter and analyze correlations or phase transitions. It’s a challenging but rewarding field — exploring these fluctuations helps us better understand the early universe conditions and the fundamental behavior of matter under extreme energy densities. 🚀

That’s a really exciting question — combining AI with physics-informed learning opens up huge possibilities! ⚡ These methods let models respect physical laws (like conservation or symmetry) while still learning from data. So even when the dataset is noisy or limited, the model doesn’t just “guess” — it stays grounded in real physics. This approach can help uncover hidden relationships in complex systems — for example, finding unknown parameters, reconstructing dynamics, or testing new hypotheses from experimental data. In short, it bridges the gap between data-driven learning and physical understanding, turning AI into a real discovery tool rather than just a pattern recognizer. 🌌

That’s a really interesting area — black hole thermodynamics shows how gravity and heat laws connect. When studying phase transitions, researchers look at how temperature, pressure, and entropy change, similar to normal materials. The extended phase space helps see these changes more clearly and understand when the system becomes unstable. This direction has good potential — it helps us get closer to understanding the microscopic nature of gravity and the link between thermodynamics and spacetime.