High-Order Numerical Methods Professors and Supervisors | ApplySharif

Find Academic Supervisors

Find and connect with academic supervisors, professors, and scholars for your research journey.

Showing 706-720 of 1,736 supervisors

Profile data may be stale. It is sourced from public faculty pages.

Taylor ErwinView profile

Academic

University of TennesseeUnited States

Taylor Erwin is a Research Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. They are affiliated with Oak Ridge National Laboratory and focus on computational fluid dynamics (CFD), high-order numerical methods, and finite-element solvers. Their work emphasizes turbulence modeling, shock capturing techniques, and aerodynamic simulation for aerospace applications. Research interests include the development of the COFFE solver within the HPCMP CREATE-AV Kestrel framework, high-performance computing optimizations, and mesh adaptation strategies. Erwin’s contributions span RANS and LES simulations, transition modeling, and validation against benchmark configurations. They collaborate on projects involving ultra-efficient aircraft design, slotted natural laminar flow concepts, and parallel computing scalability. Key technical trends in their articles include advancements in finite-element discretization, shock-capturing algorithms for discontinuous Galerkin methods, and the integration of turbulence models like Spalart-Allmaras. Recent work highlights the application of output-based mesh adaptation and high-order curved meshes for improving solution accuracy in complex flows. No scientific awards are explicitly listed in the provided information. Advising and graduate student supervision details are not mentioned. Erwin is involved with the COFFE project and the HPCMP framework, contributing to multi-level parallelism strategies and solver robustness in computational engineering. Affiliations include the University of Tennessee and Oak Ridge National Laboratory, with research activities centered on aerospace CFD challenges and high-fidelity simulation technologies.

Official University Profile

Ryan GlasbyView profile

Adjunct Professor

University of TennesseeUnited States

Ryan Glasby is a Joint Faculty member at the University of Tennessee and Oak Ridge National Laboratory (ORNL), specializing in computational engineering and high-fidelity fluid dynamics simulations. His research focuses on multiphysics modeling, hypersonic flows, shock wave interactions, and advanced numerical methods for CFD. He leads development efforts for solvers like VERTEX-CFD and Spinnaker , which are designed for high-performance computing platforms. His work spans experimental validation through techniques like acetone molecular tagging velocimetry and computational innovations in finite-element methods. Glasby collaborates closely with U.S. Department of Defense initiatives, contributing to projects such as HPCMP CREATE-AV Kestrel. Key areas include turbulence modeling, thermal nonequilibrium processes, and optimization of firearm suppression systems through fluid dynamics principles. Notable contributions include advancements in artificial compressibility methods for heat transfer simulations and strategic improvements to parallel processing algorithms for large-scale simulations. His research bridges fundamental fluid dynamics with practical applications in aerospace engineering and defense technologies.

Official University Profile

Karel MatoušView profile

Professor

University of Notre DameUnited States

Karel Matouš is a Professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame , where he also serves as the Director of the Center for Shock-Wave Processing of Advanced Reactive Materials (C-SWARM) . His research is centered on computational mechanics and engineering, with a focus on multiscale and multiphysics modeling of heterogeneous materials. Education: Ph.D. in Theoretical and Applied Mechanics, Czech Technical University in Prague (2000) M.S. in Theoretical and Applied Mechanics, Czech Technical University in Prague Research Interests: Matouš’s work spans computational science and engineering , data-driven modeling , high-performance computing , and statistical micromechanics . He develops advanced numerical methods for modeling complex systems such as solid propellants, reactive materials, and particulate composites, often integrating microtomography data for realistic material reconstruction. Publication Trends: His recent publications emphasize reduced-order modeling , image-based simulations , and uncertainty quantification in multiscale systems. Many studies combine experimental data with computational frameworks to predict macroscopic behavior from microstructural features, particularly in reactive and heterogeneous materials. Scientific Awards: Fellow of ASME (2013) Visiting Professor at Eindhoven University of Technology with 10,000 EUR research grant (2016) Rector's Award, Czech Technical University (1999) Academician Z. Bazant's Prize (1996, 1997) Multiple recognitions for high-impact publications (ScienceDirect Top 25 Hottest Articles) Student awards including the Robert J. Melosh Medal and USNCCM9 presentation prize Advising and Grants: He has advised numerous Ph.D. and M.S. students in computational mechanics and materials science. His research is supported by major grants from the Department of Energy (e.g., C-SWARM: $11.6M), NSF , DoD (STTR/SBIR programs), and industry partners like 3M and ATK . These projects focus on adaptive modeling, shock-wave processing, and microstructural characterization of advanced materials. Labs and Teams: He leads the Computational Physics Group and the C-SWARM center, which involves collaboration with institutions including Purdue University, Indiana University, and the University of Illinois. The group utilizes high-performance computing and experimental validation to advance predictive modeling of extreme material behaviors.

Official University Profile

Thomas WickView profile

Professor

Leibniz University HannoverGermany

Prof. Dr. Thomas Wick is a Professor at the Institute of Applied Mathematics within the Faculty of Mathematics and Physics at Leibniz University Hannover. He holds leadership roles, including Executive Director of the Institute and membership in the Executive Board and Faculty Council. His research focuses on numerical methods for coupled nonlinear partial differential equations, multiphysics systems (e.g., fluid-structure interaction, phase-field fracture), adaptive finite element techniques, and robust solvers. Key projects include the DFG-funded SPP 1962 and SPP 1748 initiatives, the PhoenixD Cluster of Excellence, and international collaborations like the Indo-German Higher Education Partnership. He has received grants from DFG, DAAD, and the Alexander von Humboldt Foundation. His work emphasizes algorithm design, error control, and computational efficiency in engineering and scientific applications. Research Interests: Numerical modeling of coupled PDE systems, multiphysics phenomena, phase-field fracture, adaptivity, and optimization. Projects include CoMeTeNd (IRTG 2657/1), PhoenixD Task Group S4, and Strukturerhaltende Adaptive Enriched Galerkin Methods. Scientific Awards: Feodor Lynen Fellowship, DFG Projects, DAAD grants. Collaborations span Germany, Austria, India, Peru, and France. Publications highlight advancements in phase-field fracture, fluid-structure interaction, and adaptive methods. His contributions address challenges in mesh adaptivity, error estimation, and high-performance computing.

Official University Profile

Lisa ScheunemannView profile

Assistant Professor

University of Duisburg-EssenGermany

Dr.-Ing. Lisa Scheunemann is an academic faculty member at the Institute of Mechanics, Department of Civil Engineering, Faculty of Engineering Sciences, University of Duisburg-Essen, Germany, holding the position of Akademische Rätin a.Z. She has been actively contributing to research and teaching since completing her doctorate in 2017. Research Interests: Her research focuses on advanced computational methods in solid mechanics, particularly multiscale modeling of heterogeneous materials. Key areas include the construction of statistically similar representative volume elements (RVEs) for dual-phase steels, finite element formulations for coupled micro-macro transitions, crystal plasticity modeling using the infeasible primal-dual interior point method, and the analysis of residual stresses in thermo-mechanically processed materials. Her work bridges theoretical mechanics, numerical simulation, and materials engineering. Publication Trends: Her recent publications (2019–2021) emphasize numerical algorithms for crystal plasticity, model order reduction, residual stress prediction in manufacturing, and high-performance computing applications in materials simulation. The research consistently applies computational mechanics to solve complex problems in material behavior and industrial processes. Scientific Recognition: Appointed member of GAMM Juniors (since 2019) Academic Advising and Research: She has supervised multiple master's and bachelor’s theses on topics related to computational mechanics, FEM, homogenization, and microstructure modeling. Her research is collaborative, involving projects on residual stress analysis and multiscale simulation, often in partnership with other institutions and industry-relevant engineering problems. Laboratories and Research Groups: She is affiliated with the Institute of Mechanics and participates in research initiatives such as the EXASTEEL project, which aims to create a virtual laboratory for multiscale steel simulation using high-performance computing. She is also associated with the Seminar for Numerical Mathematics and Mechanics (SNMM).

Official University Profile

Eleuterio F. ToroView profile

Professor

Medical University of GrazAustria

Eleuterio F. Toro is a Professor at the University of Trento, Italy, affiliated with the Department of Civil, Environmental and Mechanical Engineering within the College of Engineering. He is internationally recognized for his pioneering work in computational fluid dynamics and numerical methods for partial differential equations, particularly hyperbolic balance laws. His development of high-order schemes such as ADER, HLLC, and WAF has significantly advanced simulation capabilities in science and engineering. BSc Honours in Pure Mathematics, University of Warwick (1977) MSc in Applicable Mathematics, University of Dundee (1978) PhD in Computational Mathematics, University of Teesside (1982) Doctor Honoris Causa, Universidad de la Frontera, Chile (2012) His research focuses on high-resolution numerical methods, shock-capturing techniques, and more recently, the mathematical modeling of cranio-spinal fluid dynamics and its implications in neurodegenerative diseases such as Multiple Sclerosis, Parkinson’s, and Meniere’s disease. He promotes a holistic modeling approach integrating arterial blood, venous blood, interstitial fluid, cerebrospinal fluid, and the brain’s lymphatic system. The analysis of his recent publications reveals a strong trend toward interdisciplinary applications of numerical methods in biomedical contexts, especially neurofluid dynamics. His earlier works laid the foundation for modern computational fluid dynamics, while recent efforts bridge mathematics, engineering, and medicine. Scientific Awards: Doctor Honoris Causa, Universidad de la Frontera (2012) Officer of the Order of the British Empire (OBE) Toro has advised numerous students and postdoctoral researchers and has collaborated extensively with academics and medical professionals across Europe, the USA, and Chile. His work is supported by international conferences, workshops, and research collaborations. He has organized and taught intensive short courses, such as the Trento Winter School on Numerical Methods, and delivered plenary lectures at major conferences including ISMRM and CMBE. He leads research initiatives centered on mathematical modeling of body-fluid interactions, particularly in the central nervous system. His team develops computational frameworks to simulate neurovascular dynamics and test hypotheses linking venous anomalies to neurological diseases.

Official University Profile

Arnfinn Aas EielsenView profile

Associate Professor

University of StavangerNorway

Arnfinn Aas Eielsen is an Associate Professor in Electrical Engineering at the University of Stavanger, Faculty of Science and Technology, Department of Energy and Petroleum Technology. His research focuses on control systems and signal processing in precision mechatronics, particularly digital-to-analog converters (DACs), nanopositioning devices, and piezoelectric actuators. Key research areas include: Glitch mitigation in DACs using dithering and model predictive control Thermal protection for piezoelectric actuators Nonlinear damping and robust repetitive control for nanopositioning State and parameter estimation for precision systems Low-noise transimpedance amplifier design Recent publications (2023-2025) emphasize error compensation in DACs, thermal modeling, and advanced control strategies for nanoscale positioning. His work often involves collaborations with researchers in Australia and Norway, with applications in atomic force microscopy and industrial precision engineering. Current affiliations: University of Stavanger Faculty of Science and Technology Department of Energy and Petroleum Technology

Official University Profile

Peter MorseView profile

Assistant Professor

Seton Hall UniversityUnited States

Peter Morse is an Assistant Professor of Physics at Seton Hall University's Department of Physics. He joined the faculty in 2024 after completing postdoctoral research at Syracuse University, Duke University, and Princeton University. His research focuses on computational and theoretical soft matter physics, particularly the interplay between geometry, dynamics, and thermodynamics in disordered systems. Education: PhD in Physics from the University of Oregon (2016), MS in Physics from the University of Oregon (2010), and dual BS degrees in Applied and Computational Mathematical Sciences and Physics from the University of Washington (2008). Research interests include glass and jamming transitions, hyperuniformity, packing problems, and the development of computational tools for high-performance simulations. His lab emphasizes Python and C++ programming, with a focus on optimizing algorithms for studying material memory and rheology. Recent work highlights the dynamics of glass formers, hyperuniform point patterns, and computational modeling of soft matter systems. Articles frequently explore structural dynamics in high dimensions and the interplay between geometry and material properties. Students in his lab gain expertise in HPC cluster usage and algorithm development for analyzing disordered systems. Current projects include optimizing code for stealthy hyperuniform systems and studying jamming transitions in minimally structured glass formers.

Official University Profile

Mahdi Esmaily MoghadamView profile

Assistant Professor

Cornell UniversityUnited States

Mahdi Esmaily Moghadam is an Assistant Professor in the Sibley School of Mechanical and Aerospace Engineering at Cornell University, where he joined in January 2018. His research bridges computational mechanics and cardiovascular medicine, focusing on developing high-fidelity tools for surgical design and disease modeling. Research Interests: His work centers on computational fluid dynamics (CFD), cardiovascular mechanics, and biological flows. He develops advanced numerical methods—such as finite-element and time-spectral solvers—for simulating complex fluid-solid interactions in the cardiovascular system. Key areas include multi-scale modeling of red blood cells, optimization of surgical procedures like the Norwood and Assisted Bidirectional Glenn, and investigating hemolysis and turbulence effects in pediatric heart patients. He also explores cerebrospinal fluid dynamics as a potential biomarker for Alzheimer’s disease. Publication Trends: His recent articles (2023–2025) emphasize accelerating cardiovascular simulations using frequency-domain solvers, cell-resolved modeling of red blood cells, and improved stabilization techniques for CFD. These works reflect a strong trend toward real-time, high-accuracy predictive tools with direct clinical applications. Scientific Awards: Postdoctoral fellowship, Center for Turbulence Research, Stanford University Outstanding Graduate Student award, UCSD MAE Department Kaplan Fellowship, UCSD Best B.Sc. Thesis Award, Iranian Society of Mechanical Engineering Advising and Grants: While specific students are not listed, his lab conducts extensive research in computational cardiovascular engineering, supported by academic fellowships and likely federal or institutional grants. His collaborations with clinical researchers indicate interdisciplinary funding sources. He has mentored work on surgical optimization, hemodynamics, and multi-scale modeling. Labs and Teams: He leads a research group focused on computational cardiovascular mechanics at Cornell. His prior work at the Center for Turbulence Research (Stanford/NASA) and collaborations with the Predictive Science Academic Alliance Program highlight involvement in large-scale, multi-institutional research teams.

Official University Profile

Andrew BuchanView profile

Lecturer

Imperial College LondonUnited Kingdom

Dr. Andrew Buchan is an Honorary Lecturer in the Department of Earth Science and Engineering at Imperial College London , affiliated with the Faculty of Engineering . His research focuses on advanced computational methods for nuclear systems, including neutron transport modeling and multi-phase fluid flow simulations. He leads the development of codes like FETCH and RADIANT , emphasizing adaptive finite elements, reduced order models, and uncertainty quantification. Education: PhD in Nuclear Engineering, Imperial College London (2002–2006) Research Interests: Simulation of coupled neutron transport and fluid dynamics in nuclear reactors Development of novel numerical methods (e.g., spherical wavelets, Krylov solvers) Historical criticality accident analysis and reactor safety modeling Integration of AI/ML techniques for improved computational efficiency Grant Coordination: Scientific coordinator of a major grant to design the RADIANT radiation transport model. Labs/Teams: Active in the Applied Modelling and Computation Group at Imperial College.

Official University Profile

Anouar BelahcenView profile

Professor

Aalto UniversityFinland

Anouar Belahcen is a Full Professor in the Department of Electrical Engineering and Automation at Aalto University. His research focuses on magnetic materials modeling, numerical analysis of energy conversion devices, and electromechanical systems. Key interests include iron loss characterization in electrical machines, magnetomechanical coupling phenomena, and vibration/noise analysis. Belahcen leads the Computational Electromechanics research group and has pioneered advancements in anisotropic hysteresis modeling, multi-scale material characterization, and fault diagnostic methodologies using machine learning. His work bridges computational electromagnetics with practical applications in electric traction systems and renewable energy infrastructure. Expertise: Magnetic Materials, Finite Element Analysis, Energy Conversion, Condition Monitoring Key Projects: Development of thermodynamically consistent hysteresis models Optimization of high-specific-power electric machines for aerospace/automotive use Machine learning-based fault detection systems Publications emphasize cutting-edge topics like vector hysteresis modeling under multiaxial stress, bearing fault diagnostics via multi-rate vibration analysis, and novel transformerless PV-inverter designs. His research consistently addresses both fundamental theory and industrial-scale implementation challenges.

Official University Profile

Antoine AllanoreView profile

Professor

Massachusetts Institute of TechnologyUnited States

Antoine Allanore is a Professor of Metallurgy and the Heather N. Lechtman Professor of Materials Science and Engineering at the Massachusetts Institute of Technology (MIT), where he also serves as Director of the Center for Materials Research in Archaeology and Ethnology. He is affiliated with the Department of Materials Science and Engineering within MIT's School of Engineering. Professor Allanore earned a chemical engineering diploma from the École Nationale Supérieure des Industries Chimiques and an MS from the National Polytechnic Institute of Lorraine, France, in 2004. He completed his PhD at the National Polytechnic Institute in 2007 in electrochemical engineering, focusing on iron metal production by electrolysis. His research centers on process design and demonstration with applications to sustainable materials extraction and manufacturing processes. Professor Allanore specializes in alternative approaches to metals and minerals extraction, including waste-free processes for potassium fertilizer production from earth-abundant raw materials and environment-friendly metal separation techniques. His work primarily relies on using electricity to provide energy-efficient processes, combining theoretical considerations with phenomenological observations. His research spans sustainable electrochemistry, minerals separation, processing science for enhanced recycling, materials science for agronomy, and molten rare-earth electrolysis. Analysis of his recent publications reveals a strong focus on selective sulfidation processes for rare earth element separation, sustainable metallurgical approaches that reduce costs by 65-95% and greenhouse gas emissions by 60-90% compared to traditional methods, and innovative applications of electrochemical techniques for metal extraction and processing. His work demonstrates consistent interdisciplinary integration of materials science, chemical engineering, and sustainability principles. John F. Elliott Lectureship Award, Association for Iron & Steel Technology (2023) Elsevier Atlas Award (2019) Early Career Faculty Fellow, The Minerals, Metals & Materials Society (2015) Vittorio de Nora Award, The Minerals, Metals & Materials Society (2012) Professor Allanore has mentored numerous graduate students and researchers who have co-authored publications across various metallurgical domains. His laboratory, the Allanore Group, pioneers the development of new oxide and sulfide electrolytes for high productivity with reduced emissions, novel chemical metallurgy insights for sustainable processes, and applications of materials science to agronomy. The group has developed innovative techniques including molten sulfide electrolysis for copper production and approaches for transforming nutrient-rich minerals into fertilizing products with controlled release rates.

Official University Profile

Chris CantwellView profile

Professor

Imperial College LondonUnited Kingdom

Dr. Chris Cantwell is a Professor in Computational Engineering at the Department of Aeronautics, Imperial College London . He specializes in developing high-performance numerical methods and software tools for fluid dynamics and biomedical applications. His work focuses on spectral/hp element methods, machine learning integration, and open-source frameworks like Nektar++. Education : MMath in Mathematics (2005), University of Warwick MSc in Scientific Computing (2006), University of Warwick PhD in Scientific Computing (2009), University of Warwick Research Interests : High-fidelity simulation of complex flows (aerodynamics, turbomachinery, nuclear fusion) Cardiac electrophysiology modeling and biomedical engineering Machine learning for fluid dynamics and system dynamics Open-source software development (Nektar++ framework) Affiliations : Imperial College Centre for Cardiac Engineering ElectroCardioMaths Programme Quantum Engineering, Science and Technology Nektar++ Project Leadership Labs & Teams : Leading research in fluid dynamics and cardiac engineering at Imperial College Cross-disciplinary collaborations in biomedical and computational sciences

Official University Profile

James DemmelView profile

Professor

University of California, BerkeleyUnited States

James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.

Official University Profile

Melina FreitagView profile

Professor

University of PotsdamGermany

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Official University Profile