Hamiltonian Learning

Hamiltonian learning focuses on inferring the Hamiltonian function—a mathematical description of a system's energy—from observational data, aiming to improve the accuracy and efficiency of modeling dynamical systems. Current research emphasizes developing novel algorithms and neural network architectures, such as Hamiltonian neural networks and neural ordinary differential equations, often incorporating symplectic integrators to preserve the inherent energy conservation properties of Hamiltonian systems. This field is significant for advancing system identification across diverse domains, from quantum physics and materials science to robotics and control systems, by enabling more accurate and efficient modeling of complex dynamics.