The carbon pump of the world’s oceans plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the oceans for climate change analyses. State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows. We will explore the benefits of using physics informed neural networks (PINNs) for solving partial differential equations related to ocean modeling, such as the wave, shallow water, and advection-diffusion equations. PINNs account for adherence to physical laws in order to improve learning and generalization. However, in this work, we show that we observe worse training and generalizability results, contrary to recent publications.