HyperStudy© is a design exploration tool for engineers and designers. It automatically creates intelligent design variants, manages runs, and collects data.
Users are then guided to understand data trends, perform trade-off studies and optimize design performance and reliability.

HyperStudy enables users to explore, understand and improve their designs using methods such as design-of-experiments, response surface modelling and optimization. Results from these studies can be easily analyzed and interpreted using HyperStudy’s advanced post-processing and data mining capabilities.

HyperStudy’s intuitive user interface combined with its seamless integration to HyperWorks for direct model parameterization and CAE result readers simplifies the study setup.

HyperStudy helps engineers to gain a deeper understanding of a design through extensive post-processing and data-mining capabilities. This significantly simplifies the task of studying, sorting and analyzing results. Study results can be post-processed as statistical data, correlation matrices, scatter plots, box plot, interaction effect plots, histograms, and parallel coordinates among others.

Furthermore, HyperStudy guides the user in the selection of post processing methods to use based on the design objectives.


HyperStudy’s comprehensive optimization methods solve different types of design problems including multi-objective and reliability/robustness based design optimization.

These methods are:

  • Adaptive response surface method (ARSM)
  • Sequential quadratic programming
  • Genetic algorithm
  • Sequential optimization and reliability analyses (SORA)
  • Single loop approach
  • Global response surface method (GRSM)
  • Multi-objective genetic algorithm
  • ARSM based SORA
  • User-defined optimizer

Optimization studies can be performed using either exact simulation or fit model.

In addition, HyperStudy provides an API to incorporate external optimization algorithms.


  • Design of Experiments
  • Response Surface Method (Fit)
  • Optimization
  • Stochastic
  • Post-Processing and Data Mining


  • Improve Design Performance and Quality
  • Perform Trade-off Studies
  • Reduce Development Time and Costs
  • Higher Productivity through Easy-to-use Environment
  • Powerful Dataset Analyses
  • Improve Simulation Correlation

Spin’s team is at your disposal for any need