PhysicsAI

Announcements

License checkout for training, testing, and prediction will now draw 75 Altair Units.

Improved Predictions of Contours: Although numerically insignificant, physicsAI could produce animations with localized noise; the effect was most notable in deformation animations. A new smoothing scheme is being introduced to produce cleaner output predictions. The improved algorithm improves predictions by both reducing noise and improving accuracy.
Figure 1.
Enhanced Insights for Model Quality Assessment: After training, data scientists rely on numerical metrics to evaluate the accuracy of the machine learning model. New metrics include coefficient of determination (r-squared), Sprague-Geers, and absolute and percent errors of the peak values. The new metrics add to physicsAI’s broad range diagnostic tools used to appraise model quality.
Access Scalable Computing Resources with Seamless Integration to Altair One: Geometric deep learning requires substantial computing resources for training. For projects saved on Altair One Drive, training can be submitted to Altair One's HPC system with a single click. Access to the computing power you need is easy with Altair One’s scalable HPC and cloud resources.

Batch size is exposed as a training parameter.
The number of nodes and elements present in each file of the dataset is visible for review.
Improved error messages.

MAE predictions improperly ignored contributions from entities with a truth of zero.
Vector predictions produced poor results if the output values were very large or very small.

Sorting columns in the training tables are not correct for scientific notation.
Backwards compatibility with specification files created in 2022.3 is ending. New files should be created.