We propose an effective and light-weight learning algorithm, Symplectic Taylor Neural Networks (Taylor-nets), to conduct continuous, long-term predictions of a complex Hamiltonian dynamic system based on sparse, short-term observations. At the heart our algorithm is novel neural network architecture consisting two sub-networks. Both are embedded with terms in form series expansion designed symm...
