神经网络驱动的交互作用动力学参数标定
Parameter calibration with Consensus-based Optimization for interaction dynamics driven by neural networks
摘要：
我们校准的神经网络的参数，相互作用动力学模型的帮助下，基于遗传算法的全局最优化方法（CBO）。我们陈述了由神经网络驱动的交互粒子系统的一般框架，并使用ESIMAS交通实验的真实数据集测试了所提出的方法。将所得力与众所周知的物理相互作用力进行比较。此外，我们将所提出的校准过程的性能与[4]中使用随机梯度下降算法的校准过程的性能进行了比较。
英文摘要：
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.
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