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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 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.
Select a Numerical Method
Select a numerical method to use when evaluating the Sampling Fit approach.
Stochastic Methods
Numerical methods available for an Stochastic approach.
Latin HyperCube
An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling.

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View new features for HyperStudy 2024.
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Learn the basics and discover the workspace.
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Discover HyperStudy functionality with interactive tutorials.
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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.
  - 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.
    - Add a Sampling 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 Sampling Fit approach.
      - Stochastic Methods
        Numerical methods available for an Stochastic approach.
        Hammersley
        Hammersley sampling belongs to the category of quasi-Monte Carlo methods. This technique uses a quasi-random number generator, based on the Hammersley points, to uniformly sample a unit hypercube.
        Latin HyperCube
        An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling.
        Modified Extensible Lattice Sequence
        A lattice sequence is a quasi-random sequence, or low discrepancy sequence, designed to equally spread out points in a space by minimizing clumps and empty spaces.
        Simple Random
        The conventional approach of sampling is commonly called Simple Random or Monte Carlo. In Simple Random sampling, a pseudo-random number generator is used for generating random numbers from 0 to 1.
      - Edit the Run Matrix
        Edit the summary of run data stored in the run matrix by editing existing runs or adding new run data.
    - Evaluate
      Run the approach.
    - Post Processing
      View the computational results from the Sampling Fit. approach
    - Create Reports
      Package reports for data generated during the approach.
  - 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

Latin HyperCube

An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling.

The Latin HyperCube method uses a constrained or stratified sampling scheme.

Latin HyperCube sampling selects

n

different values from each of

k

variables

x 1

, …

x k

in the following manner:

The range of each random variable is divided into $n$ non-overlapping intervals on the basis of equal probability.
One value from each interval is selected at random with respect to the probability density in the interval.
The $n$ values thus obtained for $x 1$ are paired in a random manner with the $n$ values of $x 2$ . These $n$ pairs are combined in a random manner with the $n$ values of $x 3$ to form $n$ triplets and so on, until $n$ k-tuplets are formed.

Figure 1. Latin HyperCube Sampling

Usability Characteristics

A stratified sampling scheme like Latin HyperCube offers the advantage of selecting random variable values that are uniformly spread across the range of random variables while taking into account the probability density function of those random variables.
A correlation structure can be specified to reflect the correlation existing between random variables. Applying a correlation structure can be costly for a large number of input variables.

Settings

In the Specifications step, Settings tab, change method settings.


Parameter	Default	Range	Description
Number of Runs	100	> 0	Number of new designs to be evaluated.
Random Seed	1	Integer 0 to 10000	Controlling repeatability of runs depending on the way the sequence of random numbers is generated. 0 Random (non-repeatable). >0 Triggers a new sequence of pseudo-random numbers, repeatable if the same number is specified.
Apply User Correlations	On	Off or On	Apply user specified correlations on the data.