Altair HyperStudy 2024.1 Release Notes

New Features

Training PhysicsAI Models within HyperStudy
PhysicsAI ribbon-style workflow has been added in the GUI to enable training models.
Defining Precision for Numeric Design Variables
A new tool has been introduced to specify the format for numeric input variables. Format definition will work in conjunction with some mathematical models to produce meaningful values such as tolerance requirement in the manufacturing process.
Variable Table for Templatized Text Files
TPL Editor tool now has a dedicated table to conveniently edit formats and navigate to the variable location in the file.

Enhancements

Auto-Capture Variable Formats in TPL files
When Parameterized File model type is used, HyperStudy will automatically detect the user-specified formats in .tpl files and store them in the Data Format Builder settings.
Support for Dynamic Cases in FMU
FMI model type now supports both static and dynamic simulation modes. In dynamic cases, HyperStudy will generate Data Sources for both simulation data and timestep component.
Building romAI models on Vector Responses
Previously romAI (hosted library in Twin Activate and Compose) fit model was available for only scalar responses and now data sources are supported as well.
Hyperparameter Support in Custom Reports
Defining user inputs for custom reports via GUI is now available.
Post-process Reliabilities with Constraints
Reliability plot in Stochastic Approach now displays constraints if applied.

Resolved Issues

  • Timeout option for child processes was not supported on Linux.
  • In a multi-model environment, Spreadsheet model failed when multi-execution used.
  • Special characters in Windows username were not handled correctly.
  • HyperView model failed at importing variables.

Announcements

  • In optimization approach, hyperopt and status files are no longer generated. Please use deprecate flag with 8157 to restore the legacy behavior.
  • Xopt is no longer supported.
  • Next release, Data Source Tool and Hstp Reader will be deprecated and will be entirely removed in 2025.0 release.
  • Testing object, getAlgorithmUsable, has been introduced to external algorithm API to check and ensure the usability of the fit model on the chosen response. The object is derived from the new FitExtensionBase/OptExtensionBase/FillExtensionBase class which is a replacement of Extension.
    #old
class FitRandomForestExtension(hstfitext.Extension):
        def isAlgorithmUsable(self, setup, algorithm):
               if algorithm == 'RF':
                  return ""
#new
class FitRandomForestExtension(hstfitext.FitExtensionBase):
         def getAlgorithmUsable(self, setup, algorithm):
                import alt.hst.algobase.hstalgocommon as hstalgocommon
                useInfo = hstalgocommon.CommonUsabilityInformation()
                if algorithm == 'RF':
                    # error & block
                    useInfo.addError('Error reason')
                    # warning but allow
                    useInfo.addWarning('Warning reason')

          return useInfo