Confidence Score

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.