Berk Hadzhamolla

• Stasik, Alexander Johannes; Hadzhamolla, Berk & Riemer-Sørensen, Signe (2026). Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations | PHM Society European Conference. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 09(01). doi: 10.36001/phme.2026.v9i1.4991. Full text in Research Archive Show summary

  Accurate modeling and forecasting of power transformer thermal behavior are critical for ensuring reliability, extending asset lifetime, and enabling optimized power system operation. Numerical approaches, such as finite element methods (FEM) and computational fluid dynamics (CFD), offer high fidelity but suffer from prohibitive computational costs, complex mesh generation, and limited feasibility in real-time or large scale applications, as well as often unknown geometries. Lumped-parameter thermal models provide a more practical alternative but depend on transformer-specific thermal constants and often fail to capture dynamic responses under varying operating and environmental conditions. Purely data-driven machine learning (ML) methods, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent architectures such as long short-term memory (LSTM) networks, have shown success in forecasting transformer oil, winding, and hotspot temperatures; however, they typically require large volumes of high-quality training data and risk producing physically inconsistent or uninterpretable results. To overcome these limitations, hybrid frameworks such as physics-informed neural networks (PINNs) embed physical laws into the learning process, enabling physically consistent solutions while reducing data demands. This paper applies a physics-aware modeling of Neural Ordinary Differential Equations (Neural ODEs) adapted for forecasting transformer thermal behavior using real-world time-series data. Neural ODEs model system dynamics in continuous time, enabling smoother predictions, robustness to irregular sampling, and improved extrapolation capabilities compared to discrete-time models such as LSTMs. A key contribution of this work is the integration of simplified heat-transfer equations for power transformers directly into the Neural ODE, enabling a physics-aware formulation of the thermal dynamics. The model’s performance and generalization capabilities are evaluated across datasets from fifteen distinct transformers located in different regions of Norway, and characterized by varying designs and cooling mechanisms. The results demonstrate the success of the developed Neural ODEs framework to serve as a standardized, physics-aware, and robust forecasting tool for heterogeneous transformer units.

• Hadzhamolla, Berk; Stasik, Alexander Johannes & Alaliyat, Saleh Abdel-Afou (2025). Modeling Dynamical Systems Using Neural ODEs: A Study On Scientific Machine Learning Models. In Scarpa, Marco; Cavalieri, Salvatore; Serrano, Salvatore & Vita, Fabrizio De (Ed.), Proceedings of the 39th ECMS International Conference on Modelling and Simulation (ECMS 2025), June 24th - June 27th, 2025, Catania, Italy. ECMS European Council for Modelling and Simulation. ISSN 9783937436869. doi: 10.7148/2025-0704. Full text in Research Archive Show summary

  This paper explores the application of Neural Ordinary Differential Equations (Neural ODEs) for modeling and simulating complex dynamic systems. We begin with the classical 1D harmonic oscillator to show how Neural ODEs can learn unknown system parameters and initial conditions directly from data, achieving accurate and robust performance under various external forces. Extending our investigation to the chaotic Lorenz system, we demonstrate the model’s capacity to capture highly nonlinear and sensitive dynamics. The results highlight the ability of Neural ODEs to approximate continuous trajectories adapt to long-term dependencies inherent in chaotic systems. Our experiments show that Neural ODEs can accurately model various dynamic systems, handle chaotic behavior, and adapt to external forces. These findings highlight the potential of Neural ODEs for improving the robustness and efficiency of hybrid machine learning models in scientific applications.

View all works in NVA

• Riemer-Sørensen, Signe; Stasik, Alexander Johannes; Hadzhamolla, Berk & Lund, Halvor (2025). Hybrid AI for power transformers​. Full text in Research Archive

View all works in NVA