3 papers
cs.LG2026
Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem
Nikolaos Kollias, Nikolaos Matzakos
Locating periodic solutions of chaotic dynamical systems normally requires an initial guess close enough to the target orbit for numerical continuation or gradient-based search to…
math.DS2026
Activation Saturation and Floquet Spectrum Collapse in Neural ODEs
Nikolaos M. Matzakos
We prove that activation saturation imposes a structural dynamical limitation on autonomous Neural ODEs with saturating activations (, sigmoid, etc.): if $q…
math.DS2026
Comparing Physics-Informed and Neural ODE Approaches for Modeling Nonlinear Biological Systems: A Case Study Based on the Morris-Lecar Model
Nikolaos M. Matzakos, Chrisovalantis Sfyrakis
Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (NODEs) represent two distinct machine learning frameworks for modeling nonlinear neuronal dynam…