5 papers
An Embedded RISC-V Evaluation of Kolmogorov--Arnold Networks in Hard-Constrained Recurrent Physics-Informed Models
Enzo Nicolas Spotorno, Josafat Leal Filho
Hard-constrained recurrent physics-informed networks (HRPINNs) embed known dynamics inside a recurrent numerical integrator and restrict a neural branch to learning only the residu…
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…
Position: Certifiable State Integrity Should Be Built from Local Validity, Not Global Scale
Enzo Nicolás Spotorno, Joao R. Campos, Antônio Augusto Medeiros Fröhlich
Breakthroughs in language and vision have motivated increasingly general foundation models for time series and physical dynamics, where evidence is promising but less mature. In sa…
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…