Numerical simulation, known as Monte Carlo simulation, is another mathematical technique used to evaluate uncertainties, based on the law of propagation of uncertainty. The method simulates the propagation of uncertainties on the input quantities of a given measurement model, and then delivers a range of possible outcomes on the output quantity, i.e. the mesurand. This course provides an introduction to numerical simulation techniques, detailing the pros and cons of this approach in comparison with the conventional method.
Engineers, researchers and technicians tasked with evaluating measurement uncertainties and tests who are seeking an in-depth understanding of all the techniques used to evaluate measurement uncertainties.
By the end of the course, the participant will be able to evaluate measurement uncertainties using numerical simulation. They will understand in which cases conventional methods do not apply and give unsatisfactory results.
Principles of numerical simulation (Monte Carlo method)
| FOP01 | Measuring Instrument Management Module (MIM ) |
| FOP02 | Calibration module |
| FOP03 | Movements module |
| FOP04 | Uncertainty module |
| FOP05 | Statistics module |
| FOP06 | Administrative Management module |
| FOP07 | Further Theory and Practice Module FD X07-014 |
| FOP08 | System settings |
| FOP09 | Monitoring module |
| FOP10 | Optimu.net module |