Main new developments
3D-to-Scalar Models
- 3D-to-scalar model is a new 3D Deep Learning model which links unstructured 3D CAD (Computer Aided Design) designs data directly to scalar quantities of interest.
- This new feature will allow you to drop a new 3D CAD design into Monolith and get an instant prediction of its performance or quality under new operating conditions.
- User feedback suggested that performance data should be included in the loss function during model training, depending on your use case, it can improve prediction accuracy.
- More information can be found in this knowledge article
Add save output under different name option to Filter Numeric and Quick Columns
- We have implemented the ability to save the manipulator output from under a different name instead of overwriting the existing dataset.
Other improvements
- Improved error handling when using quick overview and random sample steps
- Resolved - duplicate steps being created when a new step is added
- Prevent overlapping windows within the Model manipulator step selector
- Loading spinner functionality for each step has been improved
- Other bug fixes
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