{"id":392609,"date":"2024-11-04T15:55:03","date_gmt":"2024-11-04T15:55:03","guid":{"rendered":"https:\/\/innovationspace.ansys.com\/forum\/forums\/reply\/392609\/"},"modified":"2024-11-04T15:55:03","modified_gmt":"2024-11-04T15:55:03","slug":"392609","status":"publish","type":"reply","link":"https:\/\/innovationspace.ansys.com\/forum\/forums\/reply\/392609\/","title":{"rendered":"Reply To: Ls-Opt Polynomial Accuracy Problem"},"content":{"rendered":"<p>&lt;p&gt;&#8221;many experiments are being predicted with positiv force values&#8221;&#8211; how are the computed response values of the design points, all negative? Model could be predicting positives values because some computed design responses were positive.&lt;\/p&gt;&lt;p&gt;Have you tried an advanced metamodel? RBF, NN? You can go to sampling box -&gt; comparision metamodel -&gt; add a few other metamodels, then tools -&gt; repair -&gt; build metamodels, and then compare them using the accuracy plots.&nbsp;&lt;\/p&gt;&lt;p&gt;Note that for a DOE run, space filling sampling with RBF\/NN could be better. If you want to run an optimization, change the task to SRSM (iterative domain reduction) and the metamodel accuracy will improve as the domain is reduced after each iteration in search of an optimum design.&nbsp;&lt;\/p&gt;<\/p>\n","protected":false},"template":"","class_list":["post-392609","reply","type-reply","status-publish","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"&quot;many experiments are being predicted with positiv force values&quot;-- how are the computed response values of the design points, all negative? 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