Citations 2
BioSwarmEV Swarm Intelligence-Based Electric Vehicle Energy Consumption Model with Biological Pattern Formation
Alevizos, Vasileios , Gerolimos, Nikitas , Yue, Zongliang , Edralin, Sabrina , Xu, Clark , Papakostas, George A. , Vrochidou, Εleni , Georlimos, Nikitas , Yue, Zongliang , Edralin, Sabrina , Xu, Clark , Papakostas, George A , Vrochidou, Eleni
Proceedings of the Sixth International Conference on Digital Age & Technological Advances for Sustainable Development | October 21, 2025 | Volume: No volume available | Issue: No issue available | Pages: 22-27
Advanced Graph–Physics Hybrid Framework (AGPHF) for Holistic Integration of AI-Driven Graph- and Physics- Methodologies to Promote Resilient Wastewater Management in Dynamic Real-World Conditions
Alevizos, Vasileios , Gerolimos, Nikitas , Yue, Zongliang , Edralin, Sabrina , Xu, Clark , Papakostas, George A. , Vrochidou, Εleni , Marnellos, George E. , Mustafa, Mousa, Marnellos, George
Applied Sciences | September 10, 2025 | Volume: 15 | Issue: 18 | Pages: 9905
Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning within a hybrid digital-twin framework, evaluated on multi-scale real-world datasets. The developed models achieved over 90% dissolved organic carbon removal and reduced aeration energy by up to 22% while maintaining process stability. The results demonstrate that graph–physics synergies not only boost operational efficiency but also reveal critical trade-offs between energy savings and hydraulic performance. Our findings establish a new benchmark for resilient, low-carbon wastewater treatment, highlighting the transformative role of data-driven system design.