Comments (2)
Does the ordinary kriging of the smt toolbox modify something?
Hi. Sorry, I am not sure to understand your question. If you try with higher dimensional problems you should see kriging performance degradation wrt KPLS performance.
Regarding the reproduction of the results of the KPLS article, as you are aware, we definitely have a problem (cf. #337, btw I fixed the link to the article).
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smt工具箱的普通克里金法是否会修改一些东西?
你好。 对不起,我不确定你的问题。 如果您尝试处理更高维度的问题,您应该会看到 KPLS 性能的克里金性能下降。
关于 KPLS 文章结果的再现,如您所知,我们肯定有问题(参见 #337 ,顺便说一句,我修复了文章的链接)。
Thank you very much!I probably found the problem.I regard the number of n_comp and the dimension of function as same value.This causes run slow.
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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
- Question About KeyError: 'design_space' HOT 1
- 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
- KPLSK is badly implemented
- Error with the prediction of variance derivatives in GPX HOT 1
- Wrong code in [7] to plot the Rosenbrock function ? HOT 2
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