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.