The focus of this study is on the generalization of neural networks, particularly in the context of sensor placement for global climate models' forecasts. The goal is to determine if sensor placement strategies derived through training a deep learning model, which is tasked with reconstructing a physical field from a set of measurements, can be effectively applied to a real high-resolution ocean global circulation model. The research compares different sensor placement methods, including one achieved using the Concrete Autoencoder method. Through modeling under varied initial conditions of the World Ocean state, it was found that sensor placements informed by deep learning methods outperformed others in forecast accuracy when using a comparable number of sensors. This finding underscores the potential of deep learning-informed sensor placement as a powerful tool for refining the predictive capabilities of global climate models and accelerating the data assimilation system without extensive revisions to their source code.