Design of a reconfigurable metamaterial with the use of Bayesian optimization and machine learning

Abstract

Metamaterials are a relatively new group of materials whose behaviour strongly depends on the design of their internal structure. They can be employed in a wide range of applications, one of which is presented in this thesis. As cardiovascular diseases account for around 30% of deaths worldwide the research done in the field of Materials Science may find a real life use in the form of a magnetically activated heart assisting device. Such a structure was designed on the basis of a newly developed magnetostrictive material with the use of finite element simulations and machine learning based analyses. The computational approach enabled the investigation of the structure’s deformation and determination of the influence of parameters, which define the metamaterial’s geometry, before commencing prototyping and experiments. Geometry, which resulted frommultiple iterations necessary to match the heart’s shape and deformation patterns, was parametrized and simulated in each configuration to create a database. Regression was performed on it with Artificial Neural Networks and Sparse Gaussian Process Regression in order to predict possible bounds and importance of specific parameters. At the subsequent stage the structure was optimized, with the aim of matching the deformation of a healthy myocardium, which concluded the project. Obtained sections of the design space allowed for qualitative as well as quantitative description of the device’s capabilities. It was established that most of the behaviour in all directions, longitudinal, radial and rotational, is determined by position of the main active element within the structure as well as its size and the size of vertical elements encompassing the myocardium. Optimization process confirmed the predictions and led to the first magnetically activated, metamaterial based design of a heart assisting device.