Data-driven methods across physical, social, and biological sciences. Sparse regression, neural networks, dynamical systems, and equation discovery.
Supervised and unsupervised learning, linear and logistic regression, PCA, clustering, and deep learning fundamentals.
Kinematics and kinetics of particles and rigid bodies. Newton's laws, energy methods, impulse and momentum, and applications to mechanical systems.
Surrogate modeling, closure modeling, equation discovery, and physics-informed neural networks applied to fluid systems.
Fluid flow in porous media: immiscible and miscible flows, Richards equation, Buckingham-Leverett equation, and applications to subsurface hydrology.