StarPINN

Project information

This project leverages Physics-Informed Neural Networks (PINNs) to solve partial differential equations (PDEs) governing stellar structure. The goal is to train a neural network using four fundamental equations that describe the internal physics of stars. Once trained, the model predicts key structural properties such as core temperature, pressure field, mass, and chemical composition, for well-known stars, including Vega, Betelgeuse, Sirius, and the Sun.

The PINN model is coded using TensorFlow, a framework that allows for building custom architectures, using specialized loss functions and performing custom training with Adam followed by BFGS (from TensorFlow Probability) as the optimizer.

Python TensorFlow astrophysics