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A fit gives you parameter values. Post-fit analysis tells you how much to trust them: how tightly the data constrains each parameter, and which parameters the model output actually responds to. Both run as pipeline elements, alongside the fit rather than after it in your own code, so the expensive part happens on the platform. For the concepts behind variance-based sensitivity, see the Sensitivity Analysis Guide.

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The two elements

Both take the same objectives and parameters as a DataFit. They accept only a PointEstimate optimizer, because they analyse a fit rather than performing one.

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Adding analysis to a pipeline

Place the analysis element after the fit it analyses. When its objectives and parameters match the fit’s, it picks up the fitted point automatically — you do not restate the values:

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Reading the results

Each element returns a typed result, reachable off the pipeline result rather than by reading raw job metadata:
Sobol sensitivity is a sampling method, so its cost grows with the number of parameters and the sample count. Start with a small parameter set and widen it once you know which parameters matter.