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 similarity score. In HyperMesh, the similarity score is displayed in the top-right
corner of the prediction window.Figure 1.
Interpret Similarity Scores
A similarity 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 similarity 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 similarity 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 data sufficiently similar to the predicted designs.
Missing Similarity Scores
There are several reasons why you might not see a similarity score:
Your training or input designs either do not have shell elements or do not
have extracted solid faces. Similarity 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.
