Forward Sensitivity Analysis of the FitzHugh–Nagumo System: Parameter Estimation

The FitzHugh–Nagumo (FHN) model, from computational neuroscience, has attracted attention in nonlinear dynamics studies as it describes the behavior of excitable systems and exhibits interesting bifurcation properties. accurate estimation model parameters is vital to understand how solution trajectory evolves time. To this end, we provide a forward sensitivity method (FSM) approach quantify main using sparse measurement data. FSM constitutes variational data assimilation technique which integrates sensitivities into process fitting observations. We analyze applicability update FHN predict its dynamical characteristics. Furthermore, highlight few guidelines for observations placement control shape cost functional improve parameter inference iterations.