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Neural Networks Enforcing Physical Symmetries in Nonlinear Dynamical Lattices: The Case Example of the Ablowitz-Ladik Model

In this work we introduce symmetry-preserving, physics-informed neural networks (S-PINNs) motivated by symmetries that are ubiquitous to solutions of nonlinear dynamical lattices. Although the use of PINNs have recently attracted much attention in data-driven discovery of solutions chiefly to partial differential equations, we demonstrate that they fail at enforcing important physical laws including symmetries of solutions and conservation laws. Through the correlation of parity symmetries in both space and time of solutions to differential equations with their group equivariant representation, we construct group-equivariant NNs which respect spatio-temporal parity symmetry. Moreover, we adapt the proposed architecture to enforce different types of periodicity (or localization) of solutions to nonlinear dynamical lattices. We do so by applying S-PINNs to the completely integrable Ablowitz-Ladik model, and performing numerical experiments with a special focus on waveforms that are related to rogue structures. These include the Kuznetsov-Ma soliton, and Akhmediev breather as well as the Peregrine soliton. Our numerical results demonstrate the superiority and robustness of the proposed architecture over standard PINNs.