Find Academic Supervisors

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +6 more

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +3 more

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.

Jose Manuel Dominguez AlonsoView profile

Researcher

• Earth Physics
• Computational Fluid Dynamics
• Marine Engineering
• +4 more

Jose Manuel Dominguez Alonso is a researcher at the University of Vigo , affiliated with the Faculty of Sciences and the Marine Research Center . His work focuses on Earth Physics and Smoothed Particle Hydrodynamics (SPH) applications in marine and renewable energy systems. He is part of the FA9 EphysLab research group at the Ourense campus. Education: PhD in Applied Physics from the University of Vigo (2014) under advisors Dr. Moncho Gómez Gesteira and Dr. Alejandro Jacobo Cabrera Crespo. His research explores fluid dynamics , off-shore wind turbines , wave energy converters , and fluid-structure interaction . He has developed DualSPHPhysics for high-performance computing in marine environments. His recent articles (2024-2025) emphasize teaching numerical modeling to students, 3D hydrodynamic analysis , and coupled simulation techniques . His work bridges computational methods with real-world marine engineering challenges like coastal protection and renewable energy optimization.

• Bayesian Statistics
• Spatial Epidemiology
• Disease Mapping
• +6 more

Miguel Ángel Martínez Beneito is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. His research focuses on Bayesian statistics, spatial epidemiology, and disease mapping, with applications in public health and risk cluster detection. Education: PhD in Statistics from the University of Valencia (2005), thesis on statistical methods for detecting risk foci in epidemic outbreaks. His research interests include Bayesian modeling, spatial statistics, and computational epidemiology. He is a member of the Valencia Bayesian Research Group (VABAR), contributing to advanced statistical methodologies in health sciences. The recent publications attributed to him span topics in stochastic processes, biomechanics, and mathematical biology. However, there is a possibility of name disambiguation, as some works on biomechanics appear more aligned with a researcher from the University of Zaragoza. The core research at UV remains in statistical and epidemiological modeling. Scientific Contributions: Development of Bayesian methods for disease mapping. Application of statistical models to public health surveillance. Potential contributions to stochastic modeling in biological systems. He advises students in statistics and public health, though no specific advisees are listed. He has collaborated extensively with researchers in applied mathematics and biomechanics, though the nature of these collaborations requires further clarification due to potential name overlap. He is affiliated with the VABAR research group, focusing on Bayesian inference and its applications in real-world health problems.

• Computational Modelling
• Numerical Methods
• Fracture Mechanics
• +3 more

Daniel Millán serves as Professor of Strength of Materials at the Mechanical Engineering Department, School of Industry-Applied Sciences, National University of Cuyo. He concurrently holds leadership positions as Director of the Institute I CAI CONICET-UNCuyo, Director of the Mechanical Engineering Department, Independent Researcher at CONICET, and Head of the MoCCAI research group. His research spans computational modelling and advanced numerical methods for engineering, with core expertise in: High-order phase-field modeling of fracture mechanics in thin-shells Thin-shell flexoelectric metamaterials Automatic data-driven collective variables identification Multiscale mechanobiological modeling of cerebral arteries Cerebral aneurysm morphometry and rupture risk assessment Ultrasonic analysis for additive manufacturing evaluation Analysis of his 2019-2024 publications reveals dominant themes in cerebral aneurysm biomechanics (using Kirchhoff-Love shell theory for patient-specific modeling) and fracture mechanics in thin structures. His work bridges theoretical numerical methods like phase-field modeling with practical applications in biomedical engineering and material science, particularly in ultrasonic characterization of ice composites. As leader of the MoCCAI research group, he directs computational mechanics initiatives focused on developing advanced numerical techniques for solving complex engineering and biomedical problems through interdisciplinary collaboration.

Amine AmmarView profile

Professor

• Multiscale Modeling
• Rheology
• Numerical Simulation
• +4 more

Amine Ammar is a full Professor at Arts et Métiers University , where he serves as Deputy Director of the LAMPA laboratory and Scientific Director of the Doctorate at ENSAM. His academic career spans multiple institutions including Joseph Fourier University of Grenoble and ENS de Cachan. PhD in Mechanical Engineering (ENS de Cachan, 2001) Authorization to Direct Research (University Joseph Fourier, 2006) DEA in Solids & Structures (Paris VI, 1998) Engineering Degree (ENSAM Lille/Paris, 1998) A leading expert in multiscale computational modeling , his research focuses on rheological flows , dimensional reduction techniques , and digital twin technologies for polymer processing and composite materials. He pioneered the application of Proper Generalized Decomposition (PGD) in viscoelastic flow simulations and microstructure modeling. His scientific contributions have been recognized with the ESAFORM 2009 Scientific Award and Jean Mandel Prize . With an H-index of 28 (Scopus), he has supervised key research projects on polymer crystallization and fiber suspension dynamics. Co-author of Springer monograph on Stream-Tube Method (2021) Editor of Publibook on PGD for Materials Modeling (2012) As National Facilitator for Disciplinary Teaching at ENSAM, he has developed innovative courses on computational mechanics and rheology, mentoring students like S. Montésino and E. Prulière in their thesis work on polymer microstructuring.

Jorge GrasaView profile

Associate Professor

• Computational Mechanics
• Finite Element Methods
• Computational Biomechanics
• +6 more

Jorge Grasa is an Associate Professor at the Department of Mechanical Engineering, University of Zaragoza. His research focuses on computational mechanics with applications in biomechanics, food engineering, and material fatigue analysis. He holds a Ph.D. (2007) and M.Sc. (2001) in Mechanical Engineering from the University of Zaragoza. Research Interests: His work spans computational modeling of biological tissues (tendons, muscles, ocular lenses), finite element methods for soft tissue mechanics, food cooking simulation, and digital twin technologies for industrial processes. He has pioneered studies on tendon scaffold fabrication, non-contact tonometry for intraocular pressure assessment, and muscle fatigue mechanisms linked to calcium signaling. Awards: Received the Quercus Hernández Best Thesis Award from SEMNI. His research bridges engineering and medicine, with applications in presbyopia mechanisms, tendon repair strategies, and food quality optimization. Key Contributions: Developed validated models for abdominal wall hernia repair, steak cooking dynamics, and 3D muscle contraction simulations. His work integrates multi-scale approaches and machine learning (e.g., POD-NN for corneal property estimation).

Icíar AlfaroView profile

Associate Professor

• Computational Mechanics
• Meshless Methods
• Partition of Unity Methods
• +4 more

Icíar Alfaro is an Associate Professor in the Department of Mechanical Engineering at the University of Zaragoza, Spain. Her academic career focuses on computational mechanics, meshless methods, and real-time simulation techniques for engineering applications. Ph.D. in Mechanical Engineering (2004), University of Zaragoza M.Sc. in Structural Engineering (2000), Chalmers University of Technology M.Sc. in Mechanical Engineering (1998), University of Zaragoza Her research spans computational mechanics and numerical methods, with specialization in: Natural Element Method and partition-of-unity approaches Model reduction techniques for industrial processes Physics-based mixed reality simulations Proper Generalized Decomposition (PGD) frameworks She has supervised doctoral students including Siamak Niroomandi (2011), Carlos Quesada (2017), and Alberto Badías (2020), collaborating with institutions like UPC's LaCàN group and researcher Paco Chinesta.

• Numerical Methods
• Inverse Problems
• Medical Imaging
• +3 more

Juan Diego Alvarez Roman is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid (UC3M), and Director of the F.Abril Martorell College. His academic role includes leadership in both education and research. Research interests focus on numerical methods and their biomedical applications, particularly inverse problems in cardiac modeling, medical imaging (e.g., optical tomography, microwave screening), and computational techniques like radial basis functions (RBF) and level-set methods. Projects include the LocMoTIC initiative for cardiac arrhythmia localization and collaboration on 3D imaging of biological tissues. Recent work emphasizes RBF-based numerical methods for differential operators and applications in cardiac ischemia localization. His articles span applied mathematics, biomedical engineering, and computational physics. Grants: Principal investigator in LocMoTIC (2010-2013), and collaborator in projects like 'Clustering Automático de Comportamientos de Invertebrados en Libertad' (2021-2025) and 'Imagen óptica 3D ultrarrápida' (2016-2020). Patents: System/method for cardiac electrical activation reconstruction (2014). Teaching: Involved in undergraduate/postgraduate programs at UC3M's Department of Mathematics. Labs/Teams: Member of the Numerical Methods and Applications research group and affiliated with the Gregorio Millán Barbany University Institute for Modelling and Simulation.

Elías CuetoView profile

Professor

• Computational Mechanics
• Meshless Methods
• Numerical Simulation of Forming Processes
• +10 more

Elías Cueto is a Professor at the University of Zaragoza's Mechanical Engineering department and Director of the ESI Group-UZ Chair of the National Strategy on Artificial Intelligence. His work spans computational mechanics, data-driven simulations, and AI integration in engineering. Current affiliations: University of Zaragoza, Aragon Institute of Engineering Research Research Interests include: Meshless and PGD methods Real-time simulation and forming processes Computational surgery and digital twins Scientific machine learning and data assimilation Scientific Awards : Juan C. Simo Prize (2005) ESAFORM Scientific Prize (2006) CETIM Foundation Prize (2007) O. C. Zienkiewicz Award (2012) EAMBES Fellow (2020) IACM Fellow (2022) He leads projects on AI sustainability, physics-informed digital twins, and democratizing numerical simulation. His work combines computational mechanics with cutting-edge AI techniques for real-time engineering applications.

David GonzálezView profile

Professor

• Computational Mechanics
• Meshless Methods
• Numerical Simulation
• +8 more

David González is a Professor in the Department of Mechanical Engineering at the University of Zaragoza, Spain. His research focuses on computational mechanics, numerical methods, and data-driven modeling techniques. Ph.D. in Applied Mathematics (2004), University of Zaragoza Research Interests: Computational Mechanics and Meshless Methods Proper Generalized Decomposition (PGD) and Scientific Machine Learning Real-time simulation and Computational Surgery Numerical simulation of forming processes and No-Newtonian Fluids Scientific Collaborations: Collaborated with Prof. E. Cueto, Prof. M. Doblaré, and Prof. I. Alfaro on advised theses.