Simulation and Learning in Physical Systems

Applied neural networks to learn and simulate physical systems. Covered sine function approximation as a baseline, then moved to chaotic dynamics (Lorenz attractor), predator-prey dynamics (Lotka-Volterra), and equilibrium/phase transitions via the Ising model using the Metropolis algorithm. Explored how well neural networks can capture the behavior of these systems compared to analytical solutions.