[JFT152] Output of Response Values Using Surrogate Models

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JMAG allows for optimization calculation using surrogate models. Not only for optimization calculation, but the surrogate models can also be used for obtaining response values by setting the parameter values in advance. This enables running evaluations, combined with multiple design parameters, in a short time.
In this document, a macro is provided for quickly evaluating the results obtained by design parameter combinations using surrogate models. This document, as a workflow for parametric calculation using surrogate models, explains how to create a case that combines design parameters randomly, obtain response values and create graphs from the response values.


Surrogate model, Python, AI, Machine learning, Response value, Parametric, Excel, VBA, Macro

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