7 papers
Empirical Stability Analysis of Kolmogorov-Arnold Networks in Hard-Constrained Recurrent Physics-Informed Discovery
Enzo Nicolas Spotorno, Josafat Leal Filho, Antonio Augusto Medeiros Frohlich
We investigate the integration of Kolmogorov-Arnold Networks (KANs) into hard-constrained recurrent physics-informed architectures (HRPINN) to evaluate the fidelity of learned resi…
Supervised Metric Regularization Through Alternating Optimization for Multi-Regime Physics-Informed Neural Networks
Enzo Nicolas Spotorno, Josafat Ribeiro Leal, Antonio Augusto Frohlich
Standard Physics-Informed Neural Networks (PINNs) often face challenges when modeling parameterized dynamical systems with sharp regime transitions, such as bifurcations. In these…
A Dual-Stream Physics-Augmented Unsupervised Architecture for Runtime Embedded Vehicle Health Monitoring
Enzo Nicolas Spotorno, Antonio Augusto Medeiros Frohlich
Runtime quantification of vehicle operational intensity is essential for predictive maintenance and condition monitoring in commercial and heavy-duty fleets. Traditional metrics li…
White-Box Neural Ensemble for Vehicular Plasticity: Quantifying the Efficiency Cost of Symbolic Auditability in Adaptive NMPC
Enzo Nicolas Spotorno, Matheus Wagner, Antonio Augusto Medeiros Frohlich
We present a white-box adaptive NMPC architecture that resolves vehicular plasticity (adaptation to varying operating regimes without retraining) by arbitrating among frozen, regim…
Verifying Physics-Informed Neural Network Fidelity using Classical Fisher Information from Differentiable Dynamical System
Josafat Ribeiro Leal Filho, Antônio Augusto Fröhlich
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations and modeling physical systems by embedding physical laws into the learni…
Hard-Constrained Neural Networks with Physics-Embedded Architecture for Residual Dynamics Learning and Invariant Enforcement in Cyber-Physical Systems
Enzo Nicolás Spotorno, Josafat Leal Filho, Antônio Augusto Fröhlich
This paper presents a framework for physics-informed learning in complex cyber-physical systems governed by differential equations with both unknown dynamics and algebraic invarian…