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…
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…
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…