Congratulations to Gregory Duthé for his successful PhD Defense
Gregory has successfully defended his PhD thesis. His research focuses on Geometric Deep Learning for Wind Energy, bridging the gap between computational modeling and turbine diagnostics to enhance the efficiency and longevity of wind farms.
On March 7th 2025, Gregory Duthé successfully defended his PhD thesis, titled "Geometric Deep Learning for Wind Energy: From Farm-Scale Modeling to Turbine Diagnostics." His research explores the application of Geometric Deep Learning (GDL) methods to wind energy systems, tackling key challenges in wake-induced fatigue modeling and turbine condition monitoring. By developing Graph Neural Network (GNN) surrogate models and Transformer-based monitoring techniques, his work aims to optimize wind farm layouts, predictive maintenance strategies, and real-time asset monitoring.
Gregory’s thesis was supervised by Prof. Dr. Eleni Chatzi (ETH Zurich) and examined by an esteemed committee of experts in structural mechanics and wind energy:
- Dr. Imad Abdallah (RTDT Laboratories)
- Dr. Sarah Barber (OST)
- Prof. Dr. Filipe Magalhães (University of Porto)
- Dr. Tuhfe Gökcen (DTU)
His research contributes to reducing the Levelized Cost of Energy (LCOE) in wind farms by integrating physics-based insights with modern machine learning techniques. In addition to advancing computational models for wind farm-scale fatigue assessment, his work proposes innovative monitoring techniques for turbine blade erosion detection and offshore cable protection systems.
We extend our congratulations to Gregory on this significant achievement and look forward to the future impact of his research on sustainable wind energy systems.
👉 Learn more about Gregory’s work:
🔗 ETH Zurich Profile
🔗 external page Google Scholar
