Search Results for author:
Found 2 papers, 0 papers with code
On Active Learning for Gaussian Process-based Global Sensitivity Analysis
We propose a novel strategy for active learning that focuses on resolving the main effects of the Gaussian process (associated with the numerator of the Sobol index) and compare this with existing strategies based on convergence in the total variance (the denominator of the Sobol index).
Learning thermodynamically constrained equations of state with uncertainty
Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
