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20202026
most citedRobust Power System State Estimation using Physics-Informed Neural Networks

21 citations · 67 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks

Solon Falas, Markos Asprou, Charalambos Konstantinou +1

Power System State Estimation (PSSE) converts geographically distributed measurements into the voltage magnitudes and phase angles needed for grid monitoring and control. Learned e…

cs.LG2025★ 21 cited

Robust Power System State Estimation using Physics-Informed Neural Networks

Solon Falas, Markos Asprou, Charalambos Konstantinou +1

Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-a…

cs.LG2023★ 16 cited

Physics-Informed Neural Networks for Accelerating Power System State Estimation

Solon Falas, Markos Asprou, Charalambos Konstantinou +1

State estimation is the cornerstone of the power system control center since it provides the operating condition of the system in consecutive time intervals. This work investigates…

cs.CR2020★ 14 cited

Physics-Informed Neural Networks for Securing Water Distribution Systems

Solon Falas, Charalambos Konstantinou, Maria K. Michael

Physics-informed neural networks (PINNs) is an emerging category of neural networks which can be trained to solve supervised learning tasks while taking into consideration given la…

cs.CR2020★ 16 cited

A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems

Solon Falas, Charalambos Konstantinou, Maria K. Michael

Firmware refers to device read-only resident code which includes microcode and macro-instruction -level routines. For Internet-of-Things (IoT) devices without an operating system,…