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Robust surrogate model #483

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@uremes uremes commented Jun 6, 2024

Summary:

This update adds a surrogate model version that can handle numerical errors observed as nan or inf values in the discrepancies between observed and simulated data. This is implemented as a new model class RobustGPyRegression. The two main differences between this and standard GPyRegression are that this version (1) does not use evidence points with non-finite output value to update the regression model and (2) can train a GPy classifier to predict whether given input parameters result in a finite output value or not. These capabilities are demonstrated here.

The new model can be combined with the unreliable simulations wrapper #484 to handle failed simulations in BOLFI as discussed in #433.

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@uremes uremes requested a review from hpesonen June 6, 2024 21:19
uremes added 4 commits July 4, 2024 02:44
make this a separate pull request since it solves an unrelated issue. the initialisation issue becomes more acute in the failure-robust model but it still takes a bad seed for initialisation to fail
update plot gp to mask non-finite evidence so that the robust model does not need to filter these out
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