When running optimization calculations using GAs (genetic algorithm: Generation Algorithm), the population size and the maximum number of generations depend on the number of design variables. When there are many design variables, this causes an increase in the number of calculation cases as well as calculation time. In JMAG, optimization calculation times can be reduced by using surrogate models.
This document describes as the workflow for optimization calculations using surrogate models creating training data, creating the sample case, and using a surrogate model in running optimization calculations.
Surrogate model, Pre-installed script, Python, Optimization, AI, Machine learning