Physics-informed neural networks allow models to be trained by physical laws described by general nonlinear partial differential equations. However, traditional architectures struggle to solve more challenging time-dependent problems. In this work, we present a novel physics-informed framework for solving time-dependent partial differential equations. Our proposed model utilizes discrete cosine transforms to encode spatial frequencies and recurrent neural networks to process the time evolution, achieving state-of-the-art performance on the Taylor-Green vortex relative to other physics-informed baseline models.