Goals of Study
There are many different MCalibration optimization methods. This study examines the performance of the different methods.

Overview
- The different MCalibration optimization methods are examined by studying 3 different test cases.
- The optimization methods are applied, one at a time, to each test case with the same initial parameters.
- The convergence properties are then extracted and compared.
Case 1: UHMWPE and BB-Model
The following optimization algorithms reach the best fitness values: Automatic Extensive, NewUOA, Nelder-Mead (500/1000) and CMA-ES. The Automatic Extensive method converges the fastest, the CMA-ES method finds the best answer.
Case 2: UHMWPE and TNV-Model
Case 3: Silicone Rubber and TNV Model
Summary
- The Automatic Extensive and the CMA-ES optimization methods are most robust in finding the global optimal material parameters.
- When using the Automatic Extensive method, selecting “Repeat the automatic optimization until no further improvements” often helps finding the optimal set of parameters.
- The CMA-ES method often works better if realistic upper and lower bounds are specified for the material parameters.
General Recommendations
- If you just want to run a very quick calibration use "Automatic (Quick)".
- If you want a reasonably accurate calibration, but are willing to sacrifice some accuracy for speed of the calibration, then select “Automatic (Extensive)”.
- If you want the most accurate calibration possible then use one of the following methods: “Automatic (Extensive)” and select “Repeat the automatic optimization until no further improvements", or use CMA-ES.