Data-centric Engineering: integrating simulation, machine learning and statistics. Challenges and opportunities

Recent advances in machine learning, coupled with low-cost computation, availability of cheap stream-ing sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activ-ity with significant interest and investment from commercial stakeholders. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and inte-grating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical disciplines. We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. We highlight the opportunities that such an integrated vision can unlock and outline the key challenges holding back its realisation. We also discuss the bottlenecks in the translational aspects of the field and the long-term upskilling requirements for the existing workforce and future university graduates. '(c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Recent advancements in machine learning, coupled with affordable computation, readily available sensors, data storage and cloud technologies, have fueled widespread interdisciplinary research activities. A new hybrid data-centric engineering approach is emerging, integrating simulations and data to leverage the best of both worlds and having a transformative impact on the field of physics.