[JFT146] Optimization Calculation Using Surrogate Models

Sign in to download the data

Sign In

*Please prepare a license ID and password for the license administrator.
*It is different from the service for JMAG WEB MEMBER (free membership). Please be careful.
About authentication ID for JMAG website

Overview

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.

Keywords

Surrogate model, Pre-installed script, Python, Optimization, AI, Machine learning

Search Filter

  • All Categories