what is the difference between parametric analysis and optimization?

users often rely on parametric studies to analyse their device performances...


Within different parameter sets and often wonder if it’s possible to go beyond that in order to directly find the best solution. Optimization methods can help by automatically searching the design space efficiently and finding the optimum solution.

What is parametric or sensitivity analysis?

A parametric analysis or sensitivity analysis is the study of the influence of different geometric or physical parameters or both on the solution of the problem. Example: the influence of the air gap length on the magnetic force in a contactor. Flux software features powerful parameterization capabilities using a solving scenario. The scenario could be single or multi-parametric including the parameter time, and/or geometric or physical parameters.

Solving process

> Sequence of successive solving processes corresponding to different combinations of parameters.

Number of solved combinations

> Depends on the number of parameters. For instance, for a scenario with 2 parameters having 2 values each, 2²= 4 configurations will be solved; for a scenario with 10 parameters having 2 values each, 210= 1024 configurations will be solved.  

> The solutions are independent from one another and the results can be carried out on the entire set of configurations. Several configurations of the device, corresponding to several combinations of parameters or several time steps, are treated in a single Flux project.

> The influence of a specific parameter on a result can be visualized using curves. Once the model is solved it can be post processed in order to find a solution that meets specific requirements.  (For instance, obtain a specific force on the mobile part of a contactor).

Solving time

> Depends on the number of configurations solved (number of parameters). 

Discover more powerful method for FE simulation analysis!

Optimization means to automatically determine the best elements of a set the optimums in terms of a given requirement. 
Another advantage of optimization consists of efficiently exploring  the design space, even in the case of large numbers of parameters. The number of solved configurations (function evaluations) is determined by a smart searching strategy or by the design of the experiments in order to point directly to the optimum solution. If several objectives are specified, the algorithm automatically finds all of the solutions which are compromised between all of the objectives by means of Pareto frontier. 

GOT-It software features powerful optimization algorithms (Deterministic and Stochastic) that offer advanced search strategies based on successive evaluations until the optimum is reached. 

Automatically coupled to Flux, GOT-It efficiently optimizes any FE model

Differences between Parametric analysis and Optimization


Parametric analysis



Based on a user defined solving scenario

Based on advanced optimization algorithms (Deterministic or Stochastic)


Set of “unrefined” solutions=>need to post-process each one in order to find the optimum

Optimum solution or compromise solutions