Covers the modeling of fluid instabilities with linear perturbation theory and explores the origin of unpredictability in turbulence through the Navier-Stokes equations.
Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.
Explores the variational method in the Random Field Ising Model, discussing the cost function, algorithmic questions, Gibbs inequality, and the Gibbs free energy.
