Metamodelling of auxetic cellular solids with differential evolution optimisation

Numerical simulations of mechanical properties involve significant computational effort when implemented in large scale engineering design problems. The large number of computations involved can rule out many approaches due to the expense of carrying out many runs. One way of circumnavigating this problem is to replace the true system by an approximate surrogate model (metamodel), characterised by lower CPU times usage. Metamodels developed using Genetic Programming and Artificial Neural Networks have been developed in conjunction with a Differential Evolution (DE) optimisation framework, to identify optimal shape functions for auxetic honeycombs, both for hexagonal and hexachiral configurations.