TY - JOUR AB - Recently, Hamiltonian neural networks (HNN) have been introduced to incorporate prior physical knowledge when learning the dynamical equations of Hamiltonian systems. Hereby, the symplectic system structure is preserved despite the data-driven modeling approach. However, preserving symmetries requires additional attention. In this research, we enhance the HNN with a Lie algebra framework to detect and embed symmetries in the neural network. This approach allows to simultaneously learn the symmetry group action and the total energy of the system. As illustrating examples, a pendulum on a cart and a two-body problem from astrodynamics are considered. AU - Dierkes, Eva AU - Offen, Christian AU - Ober-Blöbaum, Sina AU - Flaßkamp, Kathrin ID - 37654 IS - 6 JF - Chaos SN - 1054-1500 TI - Hamiltonian Neural Networks with Automatic Symmetry Detection VL - 33 ER -