Problem
Cell-resolved blood models depend on membrane-parameter choices that are expensive to calibrate directly with full simulations. I addressed how to estimate parameter uncertainty and identifiability without prohibitive compute cost.
Approach
- Formulate inverse uncertainty quantification with Bayesian Annealed Sequential Importance Sampling.
- Train a Gaussian process surrogate to emulate expensive simulation outputs.
- Use Sobol indices to assess parameter identifiability.
- Calibrate against shear-driven RBC elongation behavior.
Key finding
The surrogate-accelerated pipeline recovered identifiable parameter groups efficiently and improved agreement with measurements compared with baseline parameter sets.
Why it matters
Credible uncertainty-aware calibration strengthens trust in predictive simulation results and clarifies which parameters need better experimental constraints.
Outputs
- Publication details are listed in the References section below.
- Calibration and identifiability workflow summary is presented on this page.