Accuracy and Quality
Is PhysicsAI accurate?
In general, the accuracy of a PhysicsAI model will improve with the amount of data, expressivity in the model (for example, width and depth), and allotted training time. However, practical considerations impose finite limits on these quantities. Assessing the quality of a trained model, for example by testing the model’s MAE metric against known values, is a standard step in the process of training models.
What is a good MAE?
MAE is the mean absolute error. MAE can be interpreted as an error measure of a prediction. For example, consider a model that predicts displacement with an MAE of 4 mm. This means any given prediction may be inaccurate, on average, by 4mm. This may be significant if the predicted displacement of engineering interest is only 5 mm, yet less consequential if a typical value is 500 mm.
What training settings should be used?
Every project is different. The default settings are good places to begin, but the best practice is to tune the settings to achieve a sufficiently high-quality model. The PhysicsAI workflows permit the repeated training of models on the same dataset to compare the outcomes across different settings. These experiments may provide empirical evidence that similar projects may achieve the best results with similar settings.
Can a PhysicsAI trained model replace a solver?
Yes and no. PhysicsAI models are designed to act as fast approximations of a solver, so in general, we do not expect solver-level accuracy. It is typically one to two orders of magnitude faster than a solver. This can be useful, even without achieving solver-level accuracy, because it allows you to rapidly analyze new design concepts. The final design should always be verified with a traditional solver. That said, PhysicsAI models can be trained to be quite accurate given the proper training data and settings.