Ideas, methods and worked examples at the intersection of physics and machine learning.
From a magnetic-field sensor to learned controls: how PINNs use physics to guide training and improve quantum sensing protocols.
Read post : Designing quantum sensors with physics-informed neural networksDensity, pressure and velocity in a relativistic shock tube: explore the Euler equations in Minkowski space and how PINNs learn fluid evolution.
Read post : Relativistic fluids: teaching neural networks to follow shocksHow PINNs learn counterdiabatic quantum controls: network outputs, physics-based losses, training and independent validation.
Read post : From solving equations to controlling quantum systemsHow physical knowledge shapes learning, how PINNs solve differential equations, and how to check whether a learned solution is reliable.
Read post : Physics-informed machine learning: from physical laws to neural solvers
