arXiv:2611.09430v1 Announce Type: new Abstract: Preserving the energy dissipation properties of gradient flow equations is important for the stability and reliability of numerical approximations, particularly in long-time simulations. In this paper, we propose a class of energy-dissipation-preserving physics-informed deep operator splitting networks (EDP-PI-DOSnets) for solving gradient flow equations. The proposed framework incorporates classical structure-preserving numerical schemes into neural network architectures, allowing the corresponding discrete energy dissipation laws to be preserved. In particular, suitable constraints are imposed on the convolution kernels to ensure that the learned discrete operators are symmetric and negative semidefinite. Furthermore, several network formulations are developed based on the Crank--Nicolson (CN), scalar auxiliary variable Crank--Nicolson (SAV/CN), and relaxed discrete variational derivative (R-DVD-1) schemes. Meanwhile, a physics-informed loss function based on the residuals of the governing equations is employed. Numerical experiments show that the predicted solutions are in good agreement with the reference solutions while satisfying the corresponding discrete energy dissipation laws. The results also suggest stable behavior during long-time recursive prediction and the potential applicability of the proposed framework to two-phase flow problems.