The inclusion matrix enables optimizers to optionally accept available run data as an input.  This can save significant time during the optimization process, as the optimizer can utilize previously run data.   This inclusion data typically comes from previously run approaches, for example, DOE or another optimization, meaning users are empowered to make sure no run data is left unused when optimizing.

The File Assistant Wizard lets you select a target file, and a guided set of dialogs produces a completely defined response.  This efficient process simplifies the response creation steps by moving the response construction syntax of the expression builder to the background.

Parallel Coordinate Plots, also known as Snake Plots, have a dedicated tab in each approach’s post-processing.  The plots feature controls to only show subsets of variables and responses as well as window filters to visualize areas of interest.  These versatile plots are very effective in mining information from large data sets.

User created parameters in HyperMesh are expanded to include the Abaqus, Nastran, and RADIOSS user profiles.  These parameters can be imported by HyperStudy from HyperMesh models as design variables.

Cross-validation is a technique used to assess the Quality of a Fit without requiring a validation matrix.   This feature is now automatic on all Fit types in HyperStudy.  While a dedicated validation matrix is still suggested for more complete checking, this time saving calculation can provide quality information, when a validation matrix is not available.

Including data gaps, duplicates, or outliers when building a Fit can lead to poor quality results.  New data filters automatically clean up data sets without manual intervention, helping ensure the Fit is not biased by bad data.