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Sampling Lecture 30 ME EN 575 Andrew Ning aning@byu.edu Outline - - PDF document
Sampling Lecture 30 ME EN 575 Andrew Ning aning@byu.edu Outline - - PDF document
Sampling Lecture 30 ME EN 575 Andrew Ning aning@byu.edu Outline Surrogate Based Optimization (SBO) Introduction Sampling Surrogate Based Optimization (SBO) Introduction What is a surrogate model? When might one use a surrogate model?
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When might one use a surrogate model? Procedure
Sample Construct Surrogate Perform Optimization Infill
Converged? Yes No Done
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Sampling
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What if you have 10 variables? We generally need to identify the most important variables.
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Latin Hypercube Sampling
We should try to place one sample in each row and each column (this is called a Latin square and the higher dimension extension a Latin hypercube).
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We also need our points to be space filling: This is an optimization problem: maximize: spread subject to: projection of samples on each axes follows a specified probability distribution (uniform shown above but can work with any).
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Where else would LHS be useful? Matlab: lhsdesign and lhsnorm (Statistics Toolbox) Using lhsdesign with icdf allows use with any distribution.
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