Home
Perform Studies
A study is a self-contained project in which models, variables, output responses, and approaches are defined.
Setup the Approaches
Once the study Setup is complete, an unlimited combination of approaches can be added to a study. A study approach is a specific set of steps taken to study the mathematical model of a design.
Setup Fit Studies
A Fit is a mathematical model that is trained by data and is capable of predicting output response variables for a given set of input variables.
Select a Numerical Method
Select a numerical method to use when evaluating the Fit.
Fit Methods
Numerical methods available for a Fit approach.
HyperKriging
Creates predictive models with data sets coming from deterministic computer simulations, an area of application commonly known as the Design and Analysis of Computer Experiments (DACE).

Welcome
What's New
View new features for HyperStudy 2024.1.
Get Started
Learn the basics and discover the workspace.
Tutorials
Discover HyperStudy functionality with interactive tutorials.
Manage Files and Data
Create, open, import, and save models.
Perform Studies
A study is a self-contained project in which models, variables, output responses, and approaches are defined.
- Setup the Study
  Before you can create approaches you must first setup your Study by defining input variables and output responses.
- Setup the Approaches
  Once the study Setup is complete, an unlimited combination of approaches can be added to a study. A study approach is a specific set of steps taken to study the mathematical model of a design.
  - Setup DOE Studies
    A DOE is a series of tests in which purposeful changes are made to the input variables to investigate their effect upon the output responses and to get an understanding of the global behavior of a design problem. By running a DOE, you can determine which factors are most influential on an output response.
  - Setup Fit Studies
    A Fit is a mathematical model that is trained by data and is capable of predicting output response variables for a given set of input variables.
    - Add a Fit Approach
      Add approach to the study.
    - Define Definition
      Define the models, input variables, and output responses to be used in the study.
    - Select a Numerical Method
      Select a numerical method to use when evaluating the Fit.
      - Fit Methods
        Numerical methods available for a Fit approach.
        Fit Automatically Selected by Training
        Selects the best available Fit from a list of available methods you have chosen. In addition to selecting the best method, Fit Automatically Selected by Training also automatically adjusts the individual settings (often called hyperparameters) to find the optimizing, predictive performance while avoiding overfitting.
        HyperKriging
        Creates predictive models with data sets coming from deterministic computer simulations, an area of application commonly known as the Design and Analysis of Computer Experiments (DACE).
        Least Squares Regression
        Creates a regression polynomial of the chosen order such that the sum of the squares of the differences (residuals) between output response values predicted by the regression model and the corresponding simulation model is minimized.
        Moving Least Squares Method (MLSM)
        Builds a weighted least squares model where the weights associated with the sampling points do not remain constant.
        Radial Basis Function
        Uses linear combinations of basis functions, such as linear, cubic, thin-plate spline, Gaussian, multiquadric, and inverse-multiquadric. These basis functions are observed to be accurate for highly nonlinear output responses but not for linear output responses.
      - Select Matrices
        Import and modify the design matrices and associated results for the creation of the approximation model.
      - Edit the Run Matrix
        Edit the summary of run data stored in the run matrix by editing existing runs or adding new run data.
      - Filter Run Data
        Use the Filter tab to filter the run data included in a Fit approach.
    - Evaluate
      Run the approach.
    - Post Processing
      View the computational results from the Fit.
    - Create Reports
      Package reports for data generated during the approach.
  - Setup Optimization Studies
    An Optimization is a mathematical procedure used to determine the best design for a set of given constraints, by changing the input variables in an automatic manner.
  - Setup Sampling Fit Studies
    A Sampling Fit is a combination of space-filling DOE method and mathematical model trained by the data generated.
  - Setup Stochastic Studies
    A Stochastic approach is a method of probabilistic analysis where the input variables are defined by a probability distribution, and consequently the corresponding output responses are not a single deterministic value, but a distribution.
  - Setup Basic Studies
    A Basic approach can be used to test nominal values and bounds by performing a nominal run, system bound check, or sweep.
  - Setup Verification Studies
    A Verification approach compares two data sets in a side by side comparison.
Learn the Approaches
Each approach in HyperStudy serves a different purpose in the design study.
Customize HyperStudy
Customize HyperStudy by registering solver scripts, functions, and optimizers, and defining user preferences files.
Keyboard Shortcuts
Keyboard shortcuts used to access HyperStudy features.
Frequently Asked Questions
This section provides quick responses to typical and frequently asked questions regarding HyperStudy.

View All Altair HyperWorks Help

HyperKriging

Creates predictive models with data sets coming from deterministic computer simulations, an area of application commonly known as the Design and Analysis of Computer Experiments (DACE).

These experiments are unique because they do not require some concepts such as replication. This approximation method is designed to tightly pass through and smoothly interpolate between the known points.

Usability Characteristics

Attempts to go through the exact sampling points, and in general, the residuals are small, if not zero. As a result, diagnostic measures using only the complete input matrix do not produce meaningful values. Cross-validation results provide some diagnostics using a special scheme using only the input points. To get detailed diagnostics on the quality of a HyperKriging Fit, it is suggested that you use a testing matrix.
Suitable for modeling highly nonlinear output response data that does not contain numerical noise.
Applicability of HyperKriging and Radial Basis Function methods are similar in terms of physics (they both are suggested for highly nonlinear output responses with no noise). It is suggested that you use HyperKriging for large studies that contain a large number of sampling points, whereas, Radial Basis Function is suggested for studies with a large number of variables.
Note: As a result, Radial Basis Function Fits are evaluated faster than HyperKriging Fits when used in approaches.
Figure 1. Comparison of a Quadratic Polynomial Model and aHyperKriging Model for a Function of a Single Variable. The plot on the left is a Least Squares Quadratic Regression, and the plot on the right is a HyperKriging model.

Settings

No settings available at this time.