4 papers
Physics-informed neural networks for parameter learning of wildfire spreading
Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou +1
Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, interve…
An interpretable wildfire spreading model for real-time predictions
Konstantinos Vogiatzoglou, Costas Papadimitriou, Konstantinos Ampountolas +3
Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trus…
Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems
Theocharis Apostolakis, Konstantinos Ampountolas
This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotiv…
Multi-gated perimeter flow control for monocentric cities: Efficiency and equity
Ruzanna Mat Jusoh, Konstantinos Ampountolas
A control scheme for the multi-gated perimeter traffic flow control problem of cities is presented. The proposed scheme determines feasible and optimally distributed input flows fo…