Our work deals with the flux identication problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of thedirect problem with observed proles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.

Our work deals with the flux identication problem for scalar conservation laws. The problem is formulated as an optimization problem, where the objective function compares the solution of thedirect problem with observed proles at a fixed time. A finite volume scheme solves the direct problem and a continuous genetic algorithm solves the inverse problem. The numerical method is tested with synthetic experimental data. Simulation parameters are recovered approximately. The tested heuristic optimization technique turns out to be more robust than classical optimization techniques.