A unified PINN framework uses residual-loss anomaly analysis to jointly locate regime transitions and estimate piecewise parameters in nonlinear dynamical systems.
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A FiLM-conditioned DeepONet trained on physics simulations, updated via transfer learning on final experimental deformation, and augmented with Ensemble Kalman Inversion delivers probabilistic time histories of degree of cure, viscosity, and process-induced deformation.
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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.
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Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network
A FiLM-conditioned DeepONet trained on physics simulations, updated via transfer learning on final experimental deformation, and augmented with Ensemble Kalman Inversion delivers probabilistic time histories of degree of cure, viscosity, and process-induced deformation.