New publication by Dr. Denise Degen
- Comparison of local and global sensitivity analysis demonstrating the need of global sensitivity analyses for robust model calibrations.
- Compensation for data sparsity through different weighting schemes.
- Physics-based machine learning approach to ensure the feasibility of the study.
- Robust sensitivity-driven model calibration to reliably identify model errors.
- Compensation of model errors through model calibration.