Data-driven material homogenisation, selection and design

数据驱动的均质化技术提供了一种创新且非常有前途的方法，将使材料建模能够与当前材料设计和工程的快速发展相匹配。有了这样的建模策略，为新材料设计制定适当的本构方程这一具有挑战性且经常是乏味的任务被神经网络代理模型所取代，该模型是通过在适当设计的材料的代表性体积单元（RVE）上的学习阶段构建的。因此，材料行为和设计的计算机建模变得更加直接和高效。这在先进的微结构设计和新型增材层制造技术以及对材料降解和生命周期研究的理解和预测的背景下尤为重要。该项目将整合多尺度建模、计算均质化和机器学习方面的重大最新发展，从而提供一个基于材料工程最新进展的强大数字孪生，旨在加速材料的研究、开发和设计。

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challenging and often tedious task of formulating the appropriate constitutive equations for new material designs is superseded by a neural network surrogate model constructed through a learning phase on the appropriately designed Representative Volume Element (RVE) of material.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.