Hamiltonian Neural Network

Hamiltonian Neural Networks (HNNs) leverage the principles of Hamiltonian mechanics to improve the accuracy and efficiency of neural network models for dynamical systems. Current research focuses on enhancing HNN architectures, such as incorporating symplectic integrators for improved energy conservation and developing variations like dissipative HNNs to handle non-conservative systems, often employing Graph Neural Networks for complex systems. This approach offers significant advantages in areas like system identification, long-term prediction, and medical image analysis by incorporating physical constraints, leading to improved generalization and reduced data requirements.