Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Explores classical and quantum mechanics, covering observables, momentum, Hamiltonian, and the Schrödinger equation, as well as quantum chemistry and the Schrödinger's cat experiment.
Explores Car-Parrinello molecular dynamics, a unified approach combining molecular dynamics and density-functional theory for simulating various systems, with a focus on historical background, technical details, and challenges in atomistic simulations.