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Inverse Design of Truss Metamaterials with Target Nonlinear Response via Machine Learning
We aim to establish an inverse mapping between the structure topology and the nonlinear stress-strain relationship of truss metamaterials using machine learning tools. Specifically, the nonlinear response is one of the physical basis of mechanical metamaterials and is essential for manipulating various properties. This project proposes the development of a computational design framework that realizes the generation of optimal structures for desired properties. This inverse
problem is explored in a data-driven manner, e.g., to quantitatively understand appropriate architectures for a given target response.
The framework will leverage
(i) the in-house numerical tools to compute and analyze the nonlinear mechanical response of a diverse truss metamaterial database;
(ii) machine learning techniques to explore
the design space and quantitatively model the relationship between design parameters and structural
properties;
(iii) optimization algorithms towards the search for optimal structures with tailored properties
The framework will leverage (i) the in-house numerical tools to compute and analyze the nonlinear mechanical response of a diverse truss metamaterial database; (ii) machine learning techniques to explore the design space and quantitatively model the relationship between design parameters and structural properties; (iii) optimization algorithms towards the search for optimal structures with tailored properties