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Felix BottView profile

Researcher

• Computational Mechanics
• Meshfree Methods
• Peridynamics
• +3 more

Felix Bott is a Researcher at Technische Universität München (TUM), currently affiliated with PainLabMunich at Rechts der Isar Hospital since September 2020. Previously, he served as a Research Associate and Teaching Assistant in the Mechanics & High Performance Computing Group at TUM from April 2018 to March 2020. His academic credentials include a Master of Science in Mechanical Engineering (TUM, 2018) and a Master de Sciences, Technologies, Santé specializing in multi-scale mechanics modeling (2019). Research Focus Bott's research specializes in computational mechanics, emphasizing mesh-free discretization techniques like Moving Kriging Collocation and Peridynamics. He develops probabilistic numerical methods for uncertainty quantification and inverse analyses, with applications in solid mechanics and engineering simulations. His work bridges theoretical computational frameworks with practical engineering challenges. Teaching & Advising As a teaching assistant, he led courses in Engineering Mechanics I (WS 2018/19) and Engineering Mechanics II (SS 2018, SS 2019). He supervised multiple student projects including Master's theses on peridynamics-based continuum modeling, Bachelor's theses on medical device mechanics, and research internships in dynamical systems visualization and impact phenomena simulation. Laboratory Affiliations Currently conducts research at PainLabMunich (Rechts der Isar Hospital), focusing on computational approaches for medical-mechanical problems. Previously contributed to the Mechanics & High Performance Computing Group at TUM, developing advanced numerical methods for engineering applications.

Ng How YongView profile

Assistant Professor

• Membrane Science and Technology
• Water Reclamation and Reuse
• Anaerobic/Aerobic Wastewater Treatment
• +3 more

Dr. Ng How Yong is an Assistant Professor at the Department of Civil and Environmental Engineering, National University of Singapore (NUS). He specializes in membrane science and wastewater treatment technologies, with a focus on membrane fouling mitigation and bioelectrochemical systems. His research integrates membrane bioreactors, anaerobic/aerobic processes, and microbial sensors for sustainable water reclamation and industrial wastewater management. Education: BEng/MEng (Civil Engineering, NUS, 1997/2000), Ph.D. (Environmental Engineering, UC Berkeley, 2002) Research interests include membrane separation processes, microbial fuel cell-based sensors for heavy metal detection, and energy recovery from wastewater. He has pioneered advancements in anaerobic ceramic membrane bioreactors and forward osmosis technologies. Key contributions span over 50 peer-reviewed publications, focusing on optimizing biological treatment systems, understanding fouling mechanisms, and developing novel desalination processes. His work bridges fundamental science and engineering applications, addressing global water scarcity challenges. Awards: IWA Fellow (2012), NUS Young Researcher Award (2009), Singapore Youth Award (2008) Dr. Ng has led multiple international conferences, including the 8th IWA Specialist Conference on Membrane Technology (2017) and the 3rd Asia-Pacific Young Water Professional Conference (2010). He serves on editorial boards of Water Research , Journal of Water Reuse and Desalination , and the International Water Association committees.

Ivan DokmanicView profile

Assistant Professor

• Machine Learning
• Signal Processing
• Inverse Problems
• +11 more

Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .

Guglielmo ScovazziView profile

Professor

• Finite element methods
• Computational fluid mechanics
• Computational solid mechanics
• +9 more

Guglielmo Scovazzi is a Professor at Duke University with appointments across multiple departments including the Department of Civil and Environmental Engineering, the Thomas Lord Department of Mechanical Engineering and Materials Science, and as Professor of Mathematics. His interdisciplinary research bridges computational mechanics, scientific computing, and engineering applications. Dr. Scovazzi earned his B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy), followed by an M.S. and Ph.D. in mechanical engineering from Stanford University. Prior to joining Duke, he was a Senior Member of the Technical Staff at Sandia National Laboratories' Computer Science Research Institute. His research focuses on developing advanced numerical methods for computational mechanics, particularly finite element methods for fluid and solid mechanics. Key areas include multiphase porous media flows, computational methods for materials under extreme conditions, turbulent flow computations, and instability phenomena. His work emphasizes creating accurate computational approaches that reduce design/analysis costs for complex engineering problems involving fluid-structure interactions and transient phenomena in complex geometries. Dr. Scovazzi's most significant recent contribution is the development of the Shifted Boundary Method, an innovative computational framework that enables efficient simulations on complex geometries without requiring boundary-fitted meshes. This method has found applications in geomechanics, energy systems, and resilient infrastructure design. Kavli Fellow, National Academy of Sciences & Kavli Foundation (2018) Presidential Early Career Award for Scientists and Engineers (PECASE), White House (2017) Early Career Award, U.S. Department of Energy, Advanced Scientific Computing Research Program (2014) Dr. Scovazzi teaches multiple courses in computational mechanics including Nonlinear Finite Element Analysis and Introduction to the Finite Element Method. His research has been supported by substantial federal funding, and he actively collaborates across disciplines to address challenging problems in energy, environment, and infrastructure resilience through advanced computational methods.

Yuri BazilevsView profile

Professor

• Computational Mechanics
• Isogeometric Analysis
• Fluid-Structure Interaction
• +5 more

Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.

• Computational Fluid Dynamics (CFD)
• Wave Hydrodynamics
• Wave Modeling
• +9 more

Hans Bihs is a Professor in the Department of Civil and Environmental Engineering, Faculty of Engineering. His research focuses on computational fluid dynamics (CFD), wave hydrodynamics, and wave-structure interaction using the open-source framework REEF3D. Key Research Areas: CFD simulations, wave modeling, floating body dynamics, ocean wave energy, aquaculture hydrodynamics, sediment transport, and high-performance computing. Projects: ERC Consolidator Grant PARTRES (2023-2028), EEA Grants Portugal SurfWave (2023), NFR KPN IPIRIS (2021-2025), EEA Baltic SolidShore (2021-2024), NTNU's MAPLE (2022-2025), and DigiCoast (2021-2024). Email: hans.bihs@ntnu.no His recent publications (2025-2020) analyze fluid-structure interaction, ship-induced waves, floating offshore wind turbines, submerged vegetation, and coastal structures using advanced CFD techniques. Topics include wave hydrodynamics, turbulence, and numerical modeling for marine and aquaculture systems.

Ismail Ben AyedView profile

Associate Professor

• Computer vision
• Optimization
• Medical image analysis
• +1 more

Ismail Ben Ayed is an Associate Professor at École de technologie supérieure (ETS) in Montreal, Canada, holding the ETS Research Chair on Artificial Intelligence in Medical Imaging. His research bridges computer vision, optimization, and medical image analysis to develop advanced algorithms for clinical applications, with particular focus on cardiac and neurological imaging. His research program centers on medical image segmentation using novel optimization techniques, graph-based methods, and deep learning models. He pioneers approaches for handling volumetric bias, shape compactness, and distribution matching in MRI and cardiac imaging, directly addressing clinical challenges in spine labeling, ventricle segmentation, and tumor detection. His work emphasizes mathematical rigor combined with practical medical relevance. Analysis of his 15 most recent publications (2014-2017) reveals dominant themes in medical image segmentation (80% of works), particularly for cardiac MRI (35%) and neurological applications (25%). Key methodological contributions include distributed optimization frameworks (20%), advanced graph cut techniques (30%), and deep learning architectures (25%), published consistently in top-tier venues including CVPR, MICCAI, and TPAMI. His scientific recognition includes: MICCAI travel award (2017) Outstanding Reviewer Award at CVPR (2015) GE innovation award (2010) He actively mentors researchers as evidenced by his recruitment of PhD students and postdocs, with research supported by the ETS Research Chair and multiple patents. His service includes chairing MICCAI 2017/2015 and IPTA 2017, plus continuous program committee roles at CVPR, ICCV, and MICCAI since 2011. Leading the ETS Research Chair on AI in Medical Imaging, he directs a collaborative team working on clinical translation of computer vision techniques. Current projects focus on cardiac motion analysis, brain tumor segmentation, and spine labeling systems with direct applications in radiology workflows.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +5 more

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

Donald FrenchView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Mathematical Biology
• +3 more

Donald French is a Full Professor in the Department of Mathematical Sciences at the University of Cincinnati, holding this position since 1999. Additionally, he has been a Contractor/Consultant at Wright-Patterson Air Force Base since 2010. He earned a PhD in Applied Mathematics from Cornell University (1985), an MS in Applied Mathematics from Cornell (1983), and a BA in Mathematics and Physics from the State University of New York at Oswego (1980). His research focuses on interdisciplinary mathematical modeling, particularly in Cellular Physiology and Neuroscience. Key areas include inverse problems in olfactory cilia dynamics, neuronal networks, and biofilms. He has also contributed extensively to numerical analysis, specializing in error analysis for finite element methods, meshfree techniques, and discontinuous Galerkin methods. Research Highlights: Development of energy-preserving numerical schemes for time-dependent PDEs in phase transitions and viscoelasticity. Analysis of meshfree and discontinuous Galerkin methods for diffusion problems. Collaboration with Steven Kleene on olfactory cilia modeling since 2001. His work spans computational neuroscience, fluid dynamics, and aerospace engineering, with recent advancements in trajectory planning for unmanned vehicles using PDE-based models. French has secured over $600,000 in NSF grants for projects like ion channel distribution modeling (2005–2009) and mathematical physiology career development (2002–2004). French has held leadership roles, including Graduate Program Director at UC (2015–2017 and 1999–2001), and served on Taft Research and departmental committees. His teaching includes advanced courses in numerical analysis, PDEs, and mathematical biology, which he helped pioneer at UC.

Yves BourgaultView profile

Professor

• Computational Fluid Dynamics
• Numerical Methods
• Finite Element Methods
• +3 more

Yves Bourgault is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a MSc and PhD from Laval University. His research focuses on computational fluid dynamics, numerical methods, finite element techniques, and continuum mechanics modeling, with applications in cardiac electrophysiology and ecological systems. Dr. Bourgault has supervised several graduate students, including Edward Boey (co-supervised), Sana Keita, Saint-Cyr Koyagurebo-Ime, and Kak Choon Loy. His work integrates advanced numerical techniques to address complex problems in biomedical engineering, environmental science, and mathematical physics. Key methodologies include finite element methods, deferred correction schemes, and anisotropic mesh adaptation. His research group is part of the Applied Mathematics division at the University of Ottawa, emphasizing interdisciplinary applications. Recent work explores climate change impacts on ecological systems, cardiac tissue modeling using high-resolution MRI data, and robust numerical methods for reaction-diffusion equations. Publications span topics such as bidomain models for cardiac electrophysiology, fluid-structure interaction in heart mechanics, and mathematical modeling of fuel cells. His contributions bridge theoretical numerical analysis with real-world biomedical and environmental challenges.

• Computational Fluid Dynamics
• Turbulence Modelling
• Multiphase Flows
• +6 more

Dr. Zhihua Xie is a Reader in the School of Engineering at Cardiff University. He holds a PhD in Computational Fluid Dynamics from the University of Leeds, funded by the Marie Curie EST Fellowship. His career includes research roles at Cardiff University and Imperial College London. His research focuses on computational fluid dynamics, multiphase flows, and environmental fluid mechanics, supported by grants from EPSRC, Royal Society, and others. He has been awarded the Alexander von Humboldt Research Fellowship and multiple Baker Medals. Education: BEng in Environmental Engineering (Dalian Maritime University, 2003), Postgraduate study in Hydrodynamics (Dalian Maritime University, 2006), PhD in CFD (University of Leeds, 2010). Research interests span development and application of CFD codes for multiphase flows, turbulence modelling, and numerical methods. He is actively involved in editorial boards and professional societies like IAHR and ISOPE. Key contributions include adaptive moment-of-fluid methods, Cartesian cut-cell techniques, and large-eddy simulations. Awards include the Alexander von Humboldt Fellowship (2023), Baker Medal (2021, 2022), and EPSRC funding for wave energy converter modeling (EP/V040235/1). Grants and projects include ARCHER2 eCSE, Newton Advanced Fellowship, and collaborations on coastal engineering and offshore energy systems. His work addresses challenges in wave-structure interaction, fluid-structure dynamics, and environmental hydraulics.

• Machine Learning
• Data-Driven Modeling
• Numerical Solutions of Partial Differential Equations
• +5 more

Kailiang Wu is an Associate Professor at the Department of Mathematics, Southern University of Science and Technology (SUSTech), and holds concurrent roles at the Shenzhen International Center for Mathematics and National Center for Applied Mathematics Shenzhen. His research bridges Machine Learning and Computational Fluid Dynamics , focusing on High-Order Numerical Methods for Hyperbolic Conservation Laws and Relativistic Astrophysics . Education: Ph.D. in Mathematics (Peking University, 2016), B.Sc. in Mathematics and Statistics (Huazhong University of Science and Technology, 2011) His work develops Structure-Preserving Schemes for multidimensional PDEs, including Oscillation-Eliminating Discontinuous Galerkin (OEDG) and Geometric Quasilinearization (GQL) frameworks. These methods ensure positivity , divergence-free , and bound-preservation in simulations of relativistic flows and MHD systems. Recent publications emphasize Deep Learning applications in operator learning (e.g., DUE framework) and Data-Driven Modeling of unknown PDEs. His group has produced 20+ peer-reviewed articles in top journals (Math. Comp., SIAM J. Numer. Anal., JCP) since 2014. Honors: SUSTech President's Research Award (2025) World's Top 2% Scientist (2024) NSFC Major Program (2023, 0.7M CNY) Shenzhen Distinguished Young Scholar (2023, 4M CNY) National Excellent Young Scholar Program (2020, 2M CNY) Zhong Jiaqing Mathematics Award (2019) He advises 10+ graduate students and postdocs, with alumni securing academic positions at Sun Yat-sen University and HKUST. His lab collaborates on relativistic hydrodynamics , traffic models , and uncertainty quantification , supported by competitive funding.

• Hydraulic Energy
• Hydroelectric Power
• CFD Simulation
• +6 more

Cécile Münch-Alligné is a Professor in Hydraulic Energy at the University of Applied Sciences and Arts Western Switzerland (HES-SO) in Sion, where she serves as the Head of the Hydroelectricity Research Group and the Renewable Energy Program. She leads the Hydro Alps Lab, which conducts applied research in hydropower combining experimental and numerical approaches. Her work focuses on enhancing the flexibility of both small and large hydropower plants, with particular emphasis on adapting these systems to the evolving energy landscape and integration of renewable energy sources. Her educational background includes a BSc in Energy and Environmental Techniques, an MSc in Engineering, and a BSc in Industrial Systems, all from HES-SO Valais-Wallis. Her research spans multiple domains within hydraulic engineering and renewable energy systems, with particular expertise in CFD simulation, numerical methods, and hydraulic machine design. Münch-Alligné's research interests primarily center around improving hydropower flexibility through innovative approaches such as hydraulic short-circuit operating modes, variable speed operation, and energy recovery systems in water networks. She investigates both large-scale pumped storage power plants and micro-hydropower systems for urban water networks, with a strong focus on practical implementation and commercialization of research findings. Her work bridges theoretical modeling with experimental validation to address real-world challenges in the energy transition. Her research has been published extensively in leading journals, covering topics from Pelton turbine dynamics and Francis turbine vortex analysis to micro-turbine implementations in drinking water networks. The publications reveal a clear trend toward enhancing operational flexibility of hydropower systems to better integrate with intermittent renewable energy sources, with increasing emphasis on practical demonstration projects and commercial applications. As Principal Investigator, she has led multiple significant research projects including the SCCER 4 WP 3.2.0 2017-2020 (Supply of Electricity), Hydrolienne pour canaux artificiels Centrale de Lavey, and SOLUTION DE TRANSFERT D'ENERGIE PAR POMPAGE-TURBINAGE A PETITE ECHELLE. These projects, totaling over 2 million CHF in funding from sources including CTI, OFEN, and industrial partners, demonstrate her ability to secure substantial research funding and collaborate effectively with both academic and industry partners. Münch-Alligné leads the Hydro Alps Lab research team, which includes numerous researchers such as Steiner Amandus, Walpen Olivier, Vaccari Aldo, and others. Her collaborative approach extends to partnerships with institutions like Stahleinbau GmbH and The Ark Energy, facilitating the transfer of knowledge from research to industry application. The lab's work spans from fundamental fluid dynamics research to full-scale demonstration projects, creating a comprehensive pipeline from theory to practical implementation.

• Fluid-Structure Interaction
• Isogeometric Analysis
• Nonlinear Structural Mechanics
• +2 more

Thomas Spenke is a Researcher at the Chair for Computational Analysis of Technical Systems (CATS) within RWTH Aachen University's Center for Computational Engineering Science. He joined the research team in October 2017 after completing his master's degree in Computational Engineering Science at RWTH Aachen, where his academic journey began in 2011 with early engagement in CATS through a 2014 project thesis on isogeometric shell elements. His educational background includes: Studies in Computational Engineering Science at RWTH Aachen University (2011–2017) Bachelor and seminar theses on isogeometric shell elements for fluid-structure interaction Master thesis on numerical methods enhancing partitioned fluid-structure interaction algorithms Dr. Spenke's research spans Fluid-Structure Interaction , Isogeometric Analysis , Nonlinear Structural Mechanics (specializing in Shell Theory), and Interface Quasi-Newton Methods . His work develops efficient computational frameworks for multi-physics problems, emphasizing partitioned approaches that decouple fluid and structural solvers while ensuring stability and convergence through advanced acceleration techniques. His 9 publications (2020–2024) reveal a consistent focus on optimizing partitioned fluid-structure interaction simulations. Key trends include quasi-Newton acceleration for interface coupling, adaptive time-stepping strategies, and spline-based space-time finite elements for moving domains. His research frequently addresses challenges in fully enclosed systems and lubricated contact scenarios, demonstrating strong interdisciplinary links between computational mathematics and mechanical engineering. As an academic advisor, Dr. Spenke has supervised four student projects: J. Zwar (2019): Adaptive time stepping for fluid-structure interaction J. Spatka (2020): Fluid-structure-contact interaction in lubricated temper rolling M. Velioglu (2021): Space-time spline-based finite elements for moving domains S. Woyda (2023): Adaptive time stepping for multi-field problems The Chair for Computational Analysis of Technical Systems operates within RWTH Aachen's Center for Computational Engineering Science, founded by Professor Marek Behr in 2004. Located in the Rogowski building since 2009, CATS focuses on computational methods for engineering systems with expertise in fluid dynamics, structural mechanics, and multi-physics coupling algorithms.

Andrea BeckView profile

Professor

• Numerical Methods
• Computational Fluid Dynamics (CFD)
• Fluid Mechanics
• +7 more

Prof. Dr.-Ing. Andrea Beck is a faculty member and Managing Director of the Institute of Aerodynamics and Gas Dynamics (IAG) at the University of Stuttgart. She leads the Numerical Methods in Fluid Mechanics working group, focusing on high-precision numerical methods for supercomputers, particularly discontinuous Galerkin (DG) methods. Her research spans fluid mechanics, aeroacoustics, plasma physics, and multiphase flows, with applications in wind energy, helicopter systems, and environmental aerodynamics. Role: Professor and Managing Director, IAG Committees: Member of the DFG Review Board, Strategy Committee for National HPC, and steering committee of High Performance Center Stuttgart. Her research emphasizes high-order methods, turbulence modeling, and data-driven approaches. She teaches courses such as 'Numerical Methods in Fluid Mechanics' and 'CFD Programming Projects', and has developed open-source software like FLEXI and HOPR for high-performance computing. Recent articles highlight advancements in entropy-stable DG methods, turbulence simulation using graph neural networks, and multiphase flow modeling. Her work integrates machine learning with CFD to enhance simulation accuracy and efficiency.