Physics-Informed Neural Networks for Parameter Recovery in the Repressilator Oscillatory Model (opens in new tab)

Parameter estimation in nonlinear biological dynamical systems is a difficult inverse problem because the governing equations are often stiff or oscillatory, the data are sparse and noisy, and the objective landscape is non-convex. Physics-informed neural networks (PINNs) offer an alternative to purely simulation-based calibration by representing state trajectories with neural networks while penalizing violations of the governingequations.ThispaperstudiestheempiricalreliabilityofPINNs for rec...