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Learn the Approaches
Each approach in HyperStudy serves a different purpose in the design study.
PhysicsAI
A PhysicsAI approach is used to build fast predictive models from simulation data.
PhysicsAI Methods

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Learn the Approaches
Each approach in HyperStudy serves a different purpose in the design study.
- DOE
  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.
- Fit (Approximation)
  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.
- Optimization
  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.
- Sampling Fit
  A Sampling Fit is a combination of space-filling DOE method and mathematical model trained by the data generated.
- Stochastic
  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.
- Basic
  A Basic approach can be used to test nominal values and bounds by performing a nominal run, system bound check, or sweep.
- Verification
  A Verification approach compares two data sets in a side by side comparison.
- PhysicsAI
  A PhysicsAI approach is used to build fast predictive models from simulation data.
  - PhysicsAI Methods
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PhysicsAI Methods

Models can be trained on data with any physics or remeshing and without design variables. HyperStudy provides high-quality simulation data with which PhysicsAI models can be trained. Please refer to Tutorial HS-1690 on how to deploy trained models in HyperStudy.

PhysicsAI ribbons are active by default. For more information, please refer to PhysicsAI help.