A unified PINN framework uses residual-loss anomaly analysis to jointly locate regime transitions and estimate piecewise parameters in nonlinear dynamical systems.
Birg´ e, Model selection via testing: an alternative to (penalized) maximum likelihood estimators, Annales de l’IHP Probabilit´ es et Statistiques 42 (3) (2006) 273–325
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Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching
A unified PINN framework uses residual-loss anomaly analysis to jointly locate regime transitions and estimate piecewise parameters in nonlinear dynamical systems.