Abstract
Computer simulation models are used extensively in scientific and engineering problems for complex design tasks and decision processes. Surrogate models generated using data-driven techniques can approximate the behavior of complex simulation models with high fidelity and can accelerate the design process. This paper presents a physics-guided learning architecture that integrates parameters extracted from physics-based simulations into the intermediate layers of a neural network to constrain the learning process during the training of surrogate models and to improve their generalization. The proposed architecture is used to develop a surrogate model for evaluating the structural integrity of the hull of an unmanned underwater vehicle. It is shown that physics-guided learning can improve generalization in less explored regions of the design space compared to black-box models. In addition, the architecture improves the explainability of the model predictions using physics-based parameters and allows the designer to make decisions based on the input and physics-based intermediate parameters.