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Uncertainty Quantification (quoFEM) / Using quoFEM for GP based Surrogate Modelling and Sensitivity Analysis,
« on: October 25, 2023, 04:55:13 AM »
Dear SimCenter Team,
I am a PhD student at the University of New South Wales, Australia. Currently, I am working on a global sensitivity analysis using a high-fidelity atmospheric model. I have considered 50 reaction rates (input parameter) as my input quantity of interest, whose effects I will be quantifying. Using 512 sets of perturbed reaction rates, I tried to run my model and perform sobol analysis. However, my sobol indices are not converging, as I think my sample size is small. Now I have decided to create a surrogate model using the Gaussian Process (GP) Regression method and then perform sobol analysis. I am new to machine learning.
My question:
1. Since my input parameter is 50 and I have 512 output data, can I use quoFEM to perform sensitivity analysis.
2. If not, then can I use quoFEM to create GP based surrogate model.
I am a PhD student at the University of New South Wales, Australia. Currently, I am working on a global sensitivity analysis using a high-fidelity atmospheric model. I have considered 50 reaction rates (input parameter) as my input quantity of interest, whose effects I will be quantifying. Using 512 sets of perturbed reaction rates, I tried to run my model and perform sobol analysis. However, my sobol indices are not converging, as I think my sample size is small. Now I have decided to create a surrogate model using the Gaussian Process (GP) Regression method and then perform sobol analysis. I am new to machine learning.
My question:
1. Since my input parameter is 50 and I have 512 output data, can I use quoFEM to perform sensitivity analysis.
2. If not, then can I use quoFEM to create GP based surrogate model.