Data-driven homogenisation of the mechanical response of solids

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• Dr Mirac Onur Bozkurt, CUED
• Friday 14 February 2025, 14:30-15:00
• Oatley 1 Meeting Room, Department of Engineering.

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As engineering materials becomes increasingly complex, accurately predicting their mechanical behaviour under diverse loading conditions presents a significant challenge. Multiscale models offer a robust solution by bridging micro- and macroscales; however, they remain impractical due to the substantial computational demand of performing microscale computations across a macroscale domain. This study explores data-driven strategies integrated with machine learning techniques to enable the efficient homogenisation of the microscopic mechanical response of porous elastomers. A micromechanical finite element model of a porous unit cell is developed within a computational homogenisation framework to generate training data. Initially, neural network-based macroscopic surrogate models are established to predict the nonlinear elastic response of a hyperelastic porous unit cell using data from micromechanical simulations. The data-driven framework is then extended to capture the time- and path-dependent response of a viscoelastic porous unit cell. In this case, a knowledge-based data-driven approach is compared to the purely data-driven strategy and demonstrates improved efficiency while maintaining accuracy in homogenising the inelastic mechanical response.

This talk is part of the Engineering - Mechanics and Materials Seminar Series series.

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