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Stefan Vandewalle is a full professor at the Department of Computer Science, Faculty of Engineering Sciences, KU Leuven. His research focuses on numerical analysis, applied mathematics, and computational methods for stochastic differential equations, wind energy modeling, and uncertainty quantification. Department Chair, KU Leuven Member, Subdivision Numerical Analysis and Applied Mathematics Member, iSi Health Institute Observer, Faculty Council of Sciences Chair, Department Council for Computer Science His recent work explores multiscale modeling, Monte Carlo methods, and data assimilation techniques. Projects include micro-macro Parareal algorithms, wind turbine aeroelasticity, and turbulent flow reconstruction for wind farms. He supervises PhD candidates and collaborates on interdisciplinary studies involving structural mechanics and renewable energy systems. Publications highlight advancements in parallel-in-time methods, stochastic optimization for tokamak reactors, and DNS-based control of turbulent flows. Key keywords: Multiscale numerical methods Uncertainty quantification Wind energy simulation Monte Carlo algorithms PDE-constrained optimization Stochastic differential equations He contributes to academic governance as a member of extended faculty boards and evaluation committees.

• Numerical Analysis
• Applied Mathematics
• Computational Fluid Dynamics
• +5 more

Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.

• Wind Energy
• Turbulent Flow Simulation
• Flow Control & Optimization
• +5 more

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +12 more

Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.

• Signal processing
• Fault detection and diagnosis
• Rotordynamics
• +7 more

Wout Weijtjens is a Research Fellow at Vrije Universiteit Brussel, affiliated with the Acoustics & Vibration Research Group in Applied Mechanics. His research focuses on structural health monitoring (SHM) of offshore wind turbines, fatigue analysis, and vibration-based damage detection using advanced signal processing and machine learning techniques. Current projects include FIRMEST (fatigue assessment of offshore wind turbine substructures) and FOOS (Forced Oscillations in turbines). His research interests span: Operational modal analysis for offshore structures Machine learning applications in SHM Fatigue life prediction under environmental variability Sensor networks for infrastructure monitoring Wind turbine dynamics under harsh conditions Recent publications demonstrate a consistent focus on developing predictive maintenance frameworks through multivariate sensor data analysis, uncertainty quantification in SHM systems, and validation of computational models against full-scale field measurements. Article trends emphasize machine learning integration with physical models for improved fatigue life assessment. Awards and recognitions include: Best Paper Award (2nd place, 2022) Poster Award (2017) Solvay Award (2015) As principal investigator on multiple grants including VLADBC7 and VLADBC9 projects, he supervises PhD candidates in vibration-based SHM and leads experimental validation at OWI-Lab's Large Climate Chamber. His team develops IoT monitoring solutions for civil infrastructure through the SMART TOWERS initiative.

• Structural Health Monitoring
• Offshore Wind Turbines
• Fatigue Analysis
• +2 more

Lesley De Cruz is a Research Fellow at Vrije Universiteit Brussel's Electronics and Informatics department within the School of Electronics and Computer Science, with concurrent research affiliation at the Royal Meteorological Institute of Belgium since 2012. Current projects include climate-resilient urban modeling through BRGEOZ464 and flood prediction systems under FWOAL1127. Research focuses on AI-driven climate modeling with expertise in nonlinear dimension reduction (DIRESA framework), urban climate resilience, and renewable energy meteorology. Key methodologies integrate deep learning with environmental physics for applications including real-time flood prediction offshore wind farm optimization LEGO-based urban climate prototyping regional climate downscaling Recent publications (2023-2025) demonstrate strong trends in geospatial AI for environmental systems, with 64% focusing on climate change impact analysis, 54% on urban applications, and 51% on control measure development. The DIRESA framework represents a significant contribution to nonlinear dimension reduction in climate informatics. Awards include: Matìère Grise Science Communication Trophy (2024) Major media recognition for breakthrough rainfall prediction model (2021) Supervises multiple PhD candidates and leads public engagement initiatives including CurieuCity and Dag Van de Wetenschap. Current grants total 12 active projects (2021-2029) with €2.8M+ funding, primarily focused on climate adaptation and sustainable technology development. Labs and teams include the Climate Informatics Group at VUB and collaborative networks with Ghent University and the Flemish Institute for Carbon-Aware Technologies.

• Climate Modeling
• Regional Climate Downscaling
• AI for Environmental Systems
• +3 more

Jo Van Caneghem is an Associate Professor at the Department of Materials Engineering within the Faculty of Engineering Technology at KU Leuven. He serves as contact person for the Sustainable Materials Processing and Recycling (SeMPeR) research group at Group T Leuven Campus, focusing on waste valorization through material flow analysis, life cycle assessment, and thermal treatment processes. His research spans chemical characterization of waste streams for critical raw material recovery, partitioning behavior of elements in thermal processes, and fate of micro-pollutants in waste treatment systems. He develops methodologies for environmental impact assessment of industrial processes with emphasis on end-of-life treatment technologies and resource recovery pathways. Recent publications demonstrate strong integration of experimental waste processing studies with sustainability metrics, particularly in recycling complex waste streams like wind turbine blades, poultry manure ash, and coffee residues. His work consistently bridges chemical engineering fundamentals with circular economy applications through life cycle thinking. Dr. Van Caneghem actively secures research funding and mentors students: Project Leadership: Promotor for 'Thermochemical processing of complex waste streams' and 'Process data models for Waste-to-Energy'; Co-promotor for 'UPscaling biogenic waste conversion' and 'Mechanochemical-Hydrometallurgical Recycling of Spent DeNox Catalysts' Academic Supervision: Co-supervisor for PhD candidate Y. Zhang on fiberglass composite waste recycling He leads the SeMPeR research group which operates advanced facilities for waste characterization and thermal treatment testing at KU Leuven's Group T Leuven Campus, driving innovation in sustainable materials processing for the circular economy.

• Waste Management and Recycling
• Life Cycle Assessment
• Material Flow Analysis
• +4 more

Prof. Damien Ernst is a faculty member at the Montefiore Institute, Department of Electrical Engineering and Computer Science, University of Liège (Belgium). His research focuses on energy systems, remote renewable energy hubs, reinforcement learning, and optimization algorithms. He actively contributes to decarbonization strategies for hard-to-abate sectors like shipping and aviation. Academic Rank: Professor University: University of Liège School: Montefiore Institute Department: Electrical Engineering and Computer Science Research interests include: Design and techno-economic analysis of remote renewable energy systems Reinforcement learning applications in energy and optimization Energy transition policy and sustainability modeling Machine learning techniques for energy system transparency Integration of nuclear, hydrogen, and battery technologies His recent publications demonstrate expertise in: Remote Renewable Energy Hub (RREH) design Small Modular Reactor (SMR) integration Asymmetric Actor-Critic algorithms Energy system optimization and modeling Grid stability and hosting capacity analysis Explainable AI for closed-domain question answering Prof. Ernst leads PhD projects at the intersection of Operations Research and Energy Systems, focusing on energy systems models' transparency and optimization techniques.

• Energy Systems
• Operations Research
• Remote Renewable Energy Hubs
• +5 more

Reza Talemi is a Professor and Head of the Elooi Research Laboratory within the SCALINT Division at the Department of Materials Engineering (MTM), Faculty of Engineering Technology, KU Leuven, Belgium. His research focuses on structural integrity of materials fabricated through advanced manufacturing techniques, with particular expertise in impact dynamics, fracture mechanics, and fatigue analysis of metallic materials. His educational background shows extensive specialization in materials science and engineering, with research focusing on structural integrity assessment of advanced materials. His work bridges experimental and numerical approaches to understand material behavior under various loading conditions. Talemi's research interests span structural integrity of advanced materials, tribo-mechanical fracture, innovative testing methods for material behavior assessment, and advanced numerical modeling of material failure. His work has significant applications in aerospace, energy, and manufacturing sectors where material integrity is critical for safety and performance. His recent publications demonstrate a strong focus on additive manufacturing technologies, particularly examining fretting fatigue behavior of additively manufactured components, residual stress characterization, and microstructural analysis. The research shows consistent advancement in understanding how advanced manufacturing processes affect material properties and structural performance. Among his notable contributions are development of specialized testing apparatus for evaluating material performance in dovetail joint configurations, advanced numerical modeling techniques for predicting material behavior, and innovative approaches to characterize material integrity under complex loading conditions. Professor Talemi actively supervises numerous PhD students and leads multiple research projects, including 'Precise advanced material processing via controlled fatigue fracture' and 'Hybrid Experimental and Numerical Framework for Monitoring Structural Integrity in Aerospace Structures,' demonstrating his leadership in both academic and applied research domains.

• Structural integrity of materials
• Advanced manufacturing techniques
• Fracture mechanics
• +7 more

Pieter-Jan Daems is a postdoctoral researcher at Vrije Universiteit Brussel's Engineering Technology department, specializing in the Acoustics & Vibrations Research Group. With an h-index of 6, his work focuses on wind turbine dynamics and machine learning applications in renewable energy systems. Active in wind turbine research (100% focus) Specializes in modal analysis (78%) and farm-wide monitoring (35%) Expertise in condition monitoring (33%) and damping analysis (19%) Engages with machine learning (22%) for anomaly detection (19%) His recent publications demonstrate a multidisciplinary approach combining mechanical engineering, data science, and renewable energy systems. Key research themes include turbine-level power prediction, wake loss analysis, and drivetrain dynamics investigation. While no formal awards are listed, his collaborations with university researchers and industry experts indicate strong academic engagement.

• Wind Energy
• Acoustics
• Vibration Analysis
• +3 more

Pietro D'Antuono is a postdoctoral researcher affiliated with the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on fatigue analysis, structural health monitoring, and offshore wind turbine systems. He has contributed to advancing methodologies for data-driven lifetime assessment of support structures and foundations, integrating physics-informed machine learning and probabilistic modeling. His work bridges mechanical engineering, civil engineering, and renewable energy systems. Research Interests: Offshore wind turbine structural dynamics Fatigue and damage mechanics Data-driven predictive models Structural health monitoring Materials degradation analysis Key Contributions: Developed the py-Fatigue open-source toolbox for fatigue assessment. Authored 24+ peer-reviewed publications and datasets on structural reliability and offshore energy systems. Awards: Best Paper Award (2nd place, ex-aequo) at an international conference (2022). Professional Activities: Presenter at conferences on topics like fatigue modeling, data-driven methodologies, and structural health monitoring. Contributed to international research collaborations in Europe and beyond.

• Offshore Wind Turbines
• Structural Health Monitoring
• Fatigue Analysis
• +5 more

Francisco Jara Avila is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering Sciences within the Engineering Technology department, specializing in wind energy systems and machine learning applications. His work focuses on improving wind turbine performance monitoring and predictive capabilities. His research interests center on wind turbine analytics , where he develops advanced methodologies for power prediction, fault detection, and performance optimization. Using techniques like deep learning, Gaussian process regression, and signal processing, his work addresses critical challenges in wind farm operations including uncertainty quantification, spatial correlation analysis, and drivetrain health monitoring. His publication record shows a clear trajectory in wind energy analytics, with increasing sophistication from basic fault detection (2023) to sophisticated uncertainty-aware power prediction frameworks (2025). His work consistently bridges theoretical machine learning approaches with practical wind farm operational challenges. While no specific awards are listed in the available information, his publications in reputable journals like IOP Journal of Physics: Conference Series and Energies demonstrate recognition in the wind energy research community. As a researcher focused on technical development rather than academic mentoring, there's no indication of student supervision activities in the available information. His work appears to be conducted within the Acoustics & Vibrations Research Group, collaborating with researchers like Helsen, Verstraeten, and Nowe on wind energy analytics projects.

• Wind turbines
• SCADA systems
• Wind power
• +3 more

Timothy Verstraeten is a postdoctoral researcher at the Vrije Universiteit Brussel , affiliated with the Engineering Technology Acoustics & Vibrations Research Group . His work bridges Wind Energy and Machine Learning , focusing on optimization, condition monitoring, and control systems for wind farms. Role: Researcher in acoustics, vibrations, and intelligent monitoring Key Projects: IOF Gear (2018–2023), Robust Fleet-Dedicated Reinforcement Learning (2017–2020) Research Interests include: Wind turbine operations (power prediction, balancing schemes) Reinforcement learning for hybrid energy systems and fleet control Condition monitoring using Gaussian processes and anomaly detection Deep learning for predictive maintenance and wake loss modeling Scientific Contributions show a focus on Wind turbines (100% Scopus), Farms (58%), and Reinforcement learning (37%), with recent publications on uncertainty quantification and hybrid energy systems. Award: ICTAI Best Student Paper (2018) He has supervised research proposals and presented at conferences like ACM E-Energy and Wind Energy Science, emphasizing control systems , power optimization , and structural health monitoring .

• Wind turbines
• Condition monitoring
• Reinforcement learning
• +4 more

Emmanuel De Jaeger is a Professor of Electrical Energy and Power Systems at the University of Louvain (UCLouvain) , Belgium, since 2012. He serves as Director of Studies for Electro-mechanical and Energy Engineering programs. Previously, he held leadership roles at Laborelec (ENGIE Lab) from 1991-2012, including Scientific Director and Technical Director of Electricity Grids. Education : 1991: Ph.D. in Electrical Engineering, UCLouvain 1985: M.Sc. in Electrical Engineering, UCLouvain His research focuses on energy systems , electrical power systems , power quality , and power electronics , with a strong emphasis on microgrids , smart grids , and renewable energy integration . Recent work includes dynamic modeling of power converters, harmonic disturbances in low-voltage grids, and optimization of energy storage systems. He actively contributes to IEC standardization committees and has published extensively on topics such as pumped storage hydropower , DC microgrid analysis , and voltage control strategies . His teaching portfolio includes courses on electrical power systems , smart grids , and dynamic modeling .

• Energy Systems
• Electrical Power Systems
• Power Quality
• +6 more

Georgios Anogiatis serves as Associate Professor at KU Leuven's Faculty of Engineering Technology, Department of Civil Engineering, based at the Bruges Campus. He heads the Hydraulics and Geotechnics subdivision and holds key committee roles including membership in the Council of the Faculty of Engineering Technology, Civil Engineering Department Council, and Faculty Council for International Policy. His research centers on soil-structure interaction dynamics, specializing in pile foundation behavior under seismic and cyclic loading. He develops continuum analytical models for geotechnical systems including retaining walls and embedded foundations, with emphasis on wave propagation in layered soils and p-y/t-z curve development. Current work integrates physics-informed neural networks and probabilistic methods to address offshore wind turbine foundation challenges. Recent publications (2023-2025) reveal a concentrated focus on offshore renewable energy infrastructure, particularly soil-pile interaction mechanisms in inhomogeneous soils, cyclic foundation response, and computational efficiency enhancements. Key themes include decommissioning strategies for wind turbine foundations, sediment bed system development for ocean basin testing, and probabilistic performance assessment under seabed variability. He leads multiple major research initiatives including the €2.1M ATHENA project (2022-2026) on analytical programming efficiency and the 2024-2029 decommissioning strategies project for offshore wind foundations. His grant portfolio demonstrates strong industry-academia collaboration in offshore renewable energy validation and coastal engineering applications. Anogiatis directs research activities at KU Leuven's Bruges Campus Hydraulics and Geotechnics group, leveraging the Coastal & Ocean Basin facility for experimental validation of offshore foundation systems. His team specializes in centrifuge testing, wave basin experiments, and computational modeling for dredging, coastal protection, and offshore renewable energy infrastructure.

• Geotechnical Engineering
• Soil-Structure Interaction
• Pile Foundations
• +4 more