Direction Mécatronique et numérique Internship R11/2019/
Learning hidden constraints within a black box simulator with machine learning
Duration
5 month
Period
2019
Context and objectives
At IFPEN, the development and use of computer codes to model complex physical phenomena is a central tool for the design of new technologies. These numerical simulators enable the prediction of output quantities of the model from sets of input variables (calibration parameters, operational conditions,…). Nevertheless, it can appear that for some ranges of the input variables the simulator does not converge. This latter can be the consequence of implementations issues of the computer code or physical modelling problems. It seems natural to try to avoid these areas and when they are not explicitly known to try to determine the domain of the input values presenting an issue.
In this context, the objective of the internship is to learn the feasible and unfeasible set of a black-box simulator from an initial set of simulations. Solving this classification problem is crucial in order to then be able to carry out further analyses in the feasible set. This allows in particular to add constraints to the construction of the experimental design
The trainee will use methods from Machine-Learning to determine the hidden constraints of the simulator and will then be able to test and improve algorithms allowing the generation of constrained experimental designs developed at IFPEN.
Required skills
Student in mathematics or computing sciences with knowledge in probability-statistics/machine learning
Location: France (Rueil-Malmaison)
Remuneration : from 800 to 1000€ gross/month
