Partitioning of unstructured grid meshes using Boltzmann machine neural networks

Properly adapted Boltzmann machine neural networks are used to devise effective unstructured grid partitioners that are capable of providing equally loaded grid subsets with minimum interface, for concurrent data-handling on parallel computers. The partitioning scheme is based on recursive bisections so that the outcome always consists of 2ⁿ partitions. Two different techniques are introduced to speed up the—otherwise costly—partitioning process and several variants are considered. In particular, a transformation of bipolar Hopfield-type neural networks is developed providing an effective multi-scale approach. Results on a number of test cases are presented in order to assess the performance of the proposed techniques.