Physics informed machine learning based applications for the stability analysis of breakwaters

One of the key aspects in designing and stability analysis of breakwater structures is predicting the stability number of their armour blocks. This study presents a novel approach called physics informed deep neural network, for the stability analysis of rubble-mound breakwaters. The present work makes two main contributions. Firstly, it proposes a method for creating hybrid combinations of theoretical models or physical models and deep neural network architectures, leveraging the advantages of both physics and data. This framework incorporates the output of physics-based simulations and observational features into a hybrid modelling setup. Secondly, the framework employs physics-based loss functions in the learning objective of these deep neural networks, which not only demonstrate lower errors on the training set but also adhere to the established physical relations. The proposed study may have the potential to address the existing limitations in this field and provide better accuracy in estimating the stability number.