Research in Natural Hazards Engineering > Uncertainty Quantification (quoFEM)

Using quoFEM for GP based Surrogate Modelling and Sensitivity Analysis,

<< < (2/2)

Sang-ri:
Hi Atish,

For the second question, I believe the overfitting is a general limitation of GP for high-dimensional inputs (rather than a limitation in specific toolboxes/packages), and its effect is highly problem-specific.

You could always try running quoFEM, because once the data files are prepared to run the sensitivity analysis, the same files can be easily used for surrogate model training. The cross-validation results are provided as an output to help you understand how well the surrogate model is trained.

quoFEM provides easy access to UQ beginners as we put some recommended setups by default, but if you would like to have more control of the surrogate training algorithm by directly working on python/matlab toolboxes, "GPy" is the Python package that quoFEM utilizes for GP training. Additionally, "UQpy" (python) and "UQlab" (matlab) are some of the well-established and maintained UQ packages that have surrogate training modules.

Best,
Sang-ri

Navigation

[0] Message Index

[*] Previous page