Fig. 6: Results of inferring the dynamics of empirical systems, including global COVID-19 transmission and pedestrian dynamics.
From: Learning interpretable network dynamics via universal neural symbolic regression
a–d Comparison of the number of cases over time in various countries or regions generated by TPSINDy, LLCeach+TPSINDy, LLCeach, and LLCtotal. e Comparison of governing equations inferred by various methods for four representative countries or regions. Note that the template in the LLCeach+TPSINDy is the induction of equations generated by our LLC for each node. f The real pedestrian movement trajectories over time, where the data before T is used for learning. g Inferred results on the pedestrian dynamics using a mainstream social force model (SFM). h Inferred results on the pedestrian dynamics using our LLC.
