Like all machine learning models, PhysicsAI models are most accurate when the design
being predicted is similar to the designs used for training.
When you make a prediction, physicsAI will quantify how similar the input design is
to the training data in the form of a confidence score. In HyperMesh, the confidence score is displayed in the top-right
corner of the prediction window. Figure 1.
Interpret Confidence Scores
A confidence score of 1.0 indicates that the input design is the same as one of the
training points. This is the maximum possible value.
A confidence score of 0.0 indicates that the input design is as different from the
nearest training point as the two farthest training points.
A negative confidence score indicates that the input design is very different from
the training data. It’s likely that the prediction will be low-quality unless a new
model is trained with designs similar to .
Figure 2.
Missing Confidence Scores
There are several reasons why you might not see a confidence score:
Your training or input designs either do not have shell elements or do not
have extracted solid faces. Confidence scores are currently only supported
in these scenarios.
Your training data contains less than two samples.
You are using physicsAI in a client other than HyperMesh.
