Although the help files are very helpful on showing how to implement the various model fitting procedures, I haven't been able to find a guide on which routine is appropriate in which scenario. Maybe this guide does exist, in which case, please just point me in the right direction. If not, I'd love to see a webinar on examples of fitting with the different available routines and comparing their relative strengths/weaknesses in given scenarios. When is the general Fit Model function appropriate, and when would more specific techniques such as the Gaussian Process or Neural network model appropriate? I'm looking for a relatively high-level overview. For context, I'm working with a deterministic computer simulation experiment trying to come up with a metamodel for our design/tolerance space.
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I'm running JMP 10 and am having some issues with performance. Monitoring system performance during creating a Space-filling Latin Hypercube DOE shows that CPU usage is at 25% and approximately 1 GB of RAM remains free. Is there a way to encourage multicore threading and high memory usage to speed up such calculations? In addition, it seems there is an issue with memory allocation, as my RAM usage remains high even after such a process has finished. I'm running with 4GB of RAM with an Intel Core i5 @ 2.50GHz processor.
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