Comments (5)
Be careful when you have multiple outputs, Gaussian Process in SMT does not consider correlation between outputs, a independant GP is built for each of the output. You could do save the GP as you want (a list of model or a different name for each) for your PSO optimization.
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Exactly
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https://github.com/SMTorg/smt/blob/master/tutorial/SMT_EGO_application.ipynb
https://colab.research.google.com/github/SMTorg/smt/blob/master/tutorial/SMT_EGO_application.ipynb
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Dear @Paul-Saves
Thanks, but I don't see anything concerning the minimization of a multiple output function. Suppose I want to use PSO to minimize the kriging surrogate model, how can I implement this on a list of models? (section 11 of tutorial)
I have a 800 * 9 array as input and 800 * 14 one as output.
https://github.com/SMTorg/smt/blob/master/tutorial/SMT_Tutorial.ipynb
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Dear @NatOnera
If I understand correctly, in my case, I should build 14 kriging surrogate models, and compile predicted outputs to build the objective function for optimization, correct?
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Related Issues (20)
- Hessian computation/Compatibility with dual numbers HOT 4
- SIGBUS (Misaligned Address Error) HOT 1
- negative axis 1 index
- Training two samples take ~480 seconds HOT 3
- OOB access in RMTB HOT 1
- SGP Gradients unsupported HOT 1
- Relevance of the warning `R is too ill conditioned...` HOT 3
- ConfigSpace vs Numpy 2.0 compatibility HOT 1
- Document parameters to improve accuracy of SGP
- Recommendation for surrogate optimization
- Multi-Fidelity Models with Variable Design Spaces
- KeyError: 'C' HOT 2
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- How to package a trained Surrogate model into an FMU?
- ConfigSpace error with Python 3.12 - TypeError: Expected float, got numpy.float64 HOT 2
- Documentation hyperlink failing HOT 1
- RMTS issue with scipy < 1.12 HOT 1
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- Error with the prediction of variance derivatives in GPX HOT 1
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