Author

A. Raptis

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Open access Sep 2026

On the Performance of Physics-Informed Neural Networks for Hemodynamic Predictions in Parameterized Vascular Stenoses

It is highlighted that while current PINNs can reliably reproduce velocity fields, their ability to capture localized pressure dynamics remains limited, indicating the need for more robust formulations.

Michail Athanasiou, A. Raptis, Christos G. Manopoulos · Computation · 0 citations

#reinforcement learning Open access Sep 2026

Dynamic defense strategies for cyber-physical systems using Stackelberg games and deep reinforcement learning in discrete and continuous time

This study presents a dual-domain defense framework that combines Stackelberg game theory with Deep Reinforcement Learning that offers a robust, adaptable toolset for CPS defense by clarifying the trade-offs between temporal abstractions in nonlinear and dynamic environments.

A. Raptis, S. Gritzalis, A. Yannacopoulos · International Journal of Inf... · 0 citations