Hamiltonian partial differential equations, and equations obtained by adding dissipative terms to them, possess geometric structures known as symplectic and conformal symplectic structures. In this talk, we present machine learning methods for learning solutions of such systems. The proposed methods possess these geometric structures at the architectural level. In addition, they can predict solutions for a variety of systems without retraining, and physics-informed losses can also be employed.