Daniyar

Hi! Although I’m not working directly with nuclear models, my research involves simulating Nova-like variable stars using PHOEBE. I often deal with similar challenges in time-dependent modeling and numerical analysis. I’d be very interested to learn more about your computational approach — it might share some useful parallels with astrophysical simulations!

Hi! That’s a really interesting question 🔭 It’s a bit outside my research area, since I mainly work on modeling Nova-like variable stars using PHOEBE, but Bayesian methods and MCMC always sounded fascinating to me. I like how these approaches can deal with uncertainties and complex data — something we also face in stellar modeling. I’ll definitely check out some of the references you mentioned, thanks for sharing!

Hi! That’s a really good question I think AI and physics-informed learning can be very powerful for analyzing complex or incomplete data. In my research, I work with stellar light curves, and sometimes the data is noisy or has gaps — machine learning could definitely help identify patterns that traditional models might miss. Combining AI with physical constraints sounds like a great way to make models both accurate and realistic!

Hi! That sounds really interesting 🔬 I haven’t worked directly with nuclear matter, but I also use simulations for my research in astrophysics. I’m modeling Nova-like variable stars using PHOEBE. It’s fascinating how similar our approaches are — using computer models to study extreme physical conditions!

Hi everyone! 👋 I’m working on my Master’s thesis about modeling Nova-like variable stars using PHOEBE to analyze and fit their light curves. Has anyone else used PHOEBE for modeling accretion disks or star spots? I’d love to hear how you approached it! 🌟