  We calibrate parameters of neural networks that model forces in interaction
dynamics with the help of the Consensus-based global optimization method (CBO).
We state the general framework of interaction particle systems driven by neural
networks and test the proposed method with a real dataset from the ESIMAS
traffic experiment. The resulting forces are compared to well-known physical
interaction forces. Moreover, we compare the performance of the proposed
calibration process to the one in [4] which uses a stochastic gradient descent
algorithm.
