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<h1>Approximations - Kriging</h1>

<table>
	<th colspan=2>Cantilever Beam Kriging Approximation Example</th>
	<tr>
		<td>Description:</td>
		<td>This model demonstrates the use of the Kriging  approximation plugin
			to fit a model to data for a cantilever beam. Optimal Latin Hypercube sampling is 
			used to fit a Kriging model with Gaussian correlation function.An optimization of 
			the cantilever beam is performed using the resulting approximation model.
			<p><br>
			Load the model and review the approximation model:
			<ul>
				<li>Right click the optimization component and select 'Approximations'
				<li>Click 'View Data' to review data points, error analysis points, and coefficient fit data
				<li>Click 'Error Analysis...' to review goodness of fit for each response
				<li>Click 'Visualize...' to raise the approximation viewer;  adjust sliders, review 2D and
					3D graphs, zoom in on specific graphs for detailed visualization
			</ul>
			Run the model.  Compare optimization results to those obtained without the approximation model
			(run optimization examples or deactivate approximation model and re-run).
		</td>
	</tr>
	<tr>
		<td>Model:</td>
		<td><a href="CantBeamNLPQL-Kriging.zmf">CantBeamNLPQL-Kriging.zmf</a></td>
	</tr>
	<tr>
		<td>Illustrated components:</td>
		<td>Optimization, Calculator</td>
	</tr>
	<tr>
		<td>Other illustrated features:</td>
		<td>
			<ul>
				<li>Approximation error analysis
				<li>Approximation interactive viewer
				<li>Optimization
			</ul>
		</td>
	</tr>
	<tr>
		<td>Simcodes needed:</td>
		<td>None</td>
	</tr>
	<tr>
		<td>Support files needed:</td>
		<td>None</td>
	</tr>
</table>

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