Data-driven material homogenisation, selection and design

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Data-driven homogenisation techniques present an innovative and highly promising approach that will enable material modelling to match the fast pace of the current advances in material design and engineering. With such modelling strategies, the challenging and often tedious task of formulating the appropriate constitutive equations for new material designs is superseded by a neural network surrogate model constructed through a learning phase on the appropriately designed Representative Volume Element (RVE) of material. As a result the computer modelling of material behaviour and design becomes more straightforward and much more efficient. This is particularly important in the context of advanced micro-structure design and novel additive layer manufacturing techniques as well as the for the understanding and prediction of material degradation and life cycle studies. This project will integrate significant recent developments in multi-scale modelling, computational homogenisation and machine learning, thus providing a powerful digital twin based on the latest advances in material engineering with the aim of accelerating the material research, development and design.