Alisher

That’s a great topic! Physics-informed AI is becoming a powerful tool for analyzing complex or incomplete data. By embedding known physical laws directly into neural networks (like in Physics-Informed Neural Networks, PINNs), the model learns solutions that stay consistent with real physics rather than just fitting data. This helps a lot when data are noisy or sparse. If you’re interested, check out Raissi et al. (2019) on PINNs and recent reviews on physics-informed machine learning— they give a solid foundation and practical examples across fluid dynamics, plasma physics, and astrophysics.

Great question! In cosmology, MCMC is used to explore how likely different parameter values are, given the observational data. The spread of samples shows which regions of parameter space fit the data best. I’d suggest checking out Trotta (2008) for a clear overview and Lewis & Bridle (2002) for practical examples. You can also experiment with emcee and (link removed) to visualize the posteriors.

Nice work! I’ve used PHOEBE 2 for modeling nova-like and CV systems — it handles disks fairly well, though getting realistic temperature gradients can be tricky. I’d suggest adjusting disk thickness and temperature profiles manually. Also, including irradiation effects can really improve the fit for high-inclination systems.

That’s a really fascinating topic! I’m not directly studying solar radio emissions myself, but I find this area of research very interesting — especially how time-dependent models can capture the dynamic nature of coronal activity. It’s impressive how much we can learn about solar plasma behavior from radio observations.

Hey everyone! I’m working on 2D classification of gravitational wave signals (using spectrograms), but the detector noise is giving me a hard time. Has anyone here dealt with noisy GW data before? What kind of preprocessing or denoising methods worked best for you before training your model — like CNNs, autoencoders, or something else? Would love to hear your experiences or tips! 🚀