• A physics-informed neural network trained without ground truths can provide accurate initial fields for numerical prediction. • The system's kinetic features are embedded into the model through our four-dimensional variational form loss function. • We show on Lorenz96 that the proposed method can be used directly for accurate data assimilation at a low computational cost.