Physics-informed regularisation procedure in neural networks: An application in blast protection engineering

Abstract

Machine learning offers the potential to enable probabilistic-based approaches to engineering design and risk mitigation. Application of such approaches in the field of blast protection engineering would allow for holistic and efficient strategies to protect people and structures subjected to the effects of an explosion. To achieve this, fast-running engineering models that provide accurate predictions of blast loading are required. This paper presents a novel application of a physics-guided regularisation procedure that enhances the generalisation ability of a neural network (PGNN) by implementing monotonic loss constraints to the objective function due to specialist prior knowledge of the problem domain. The PGNN is developed for prediction of specific impulse loading distributions on a rigid target following close-in detonation of a spherical mass of high explosive. The results are compared to those from a traditional neural network (NN) architecture and stress-tested through various data holdout approaches to evaluate its generalisation ability. In total the results show five statistically significant performance premiums, with four of these being achieved by the PGNN. This indicates that the proposed methodology can be used to improve the accuracy and physical consistency of machine learning approaches for blast load prediction.