Abstract: A genetic algorithm-based optimization method is proposed for solving the problem of nesting arbitrary shapes. Depending on the number of objects and the size of the search space, realizing an ...

Abstract: Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric ...

Abstract: Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, ...