In the last few decades, Machine Learning has experienced rapid growth thanks to the development of hardware technologies, and the improvement of optimization algorithms, and computational capabilities. It found space in a variety of businesses, such as in the engineering field, where several neural network architectures can be employed, enabling the processing of a huge amount of data and solving complex problems, such as solving partial differential equations. That is noteworthy since the partial differential equations constitute the governing laws to model the physics behind most engineering problems. Their solution is crucial for understanding the behavior of the systems, but usually is complex and requires advanced computational techniques.