جستجوی استاد راهنمای دانشگاهی

• Numerical Analysis
• Multiscale Finite Element Method
• Flow in Porous Media
• +۴ مورد دیگر

Yalchin Efendiev is a Professor and Ewing-Mobil Chair in Computational Science at Texas A&M University, leading the Institute for Scientific Computation (ISC). He holds a Ph.D. in Applied Mathematics from Caltech (1999) and a B.S. from Moscow State University (1993). His research focuses on numerical analysis, multiscale methods, and flow in porous media, with applications to reservoir simulation and geoscience. Education: Ph.D., Applied Mathematics, California Institute of Technology, 1999 B.S., Moscow State University, 1993 Research Interests: Dr. Efendiev develops advanced numerical methods for multiscale problems, including the generalized multiscale finite element method (GMsFEM), constraint energy minimizing techniques, and machine learning-driven approaches. His work addresses challenges in porous media flow, wave propagation, and heterogeneous material modeling. He also explores data-driven model reduction and uncertainty quantification. Awards: 2025 Frontiers of Science Award 2020 SIAM Fellow Fellow of AMS (2017) Fraunhofer Bessel Award (2010) Advising & Grants: He has advised over 30 Ph.D. students and postdocs, many now leading academics and industry researchers. His grants include leadership of the Numerical Porous Media SRI Center (NumPor) and roles in international collaborations. He serves on editorial boards for Journal of Computational Physics , SIAM Multiscale Modeling and Simulation , and others. Labs/Teams: He directs the Institute for Scientific Computation (ISC) and co-leads the NumPor Center at KAUST, focusing on porous media and multiscale computational methods.

Iman Borazjaniمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• High-Performance Computing
• +۲ مورد دیگر

Iman Borazjani is Associate Professor of Mechanical Engineering and Sallie and Don Davis '61 Faculty Fellow at Texas A&M University. He holds a Ph.D. in Mechanical Engineering from the University of Minnesota (2008), M.S. from Georgia Tech (2005), and B.S. from Sharif University of Technology (2002). His research focuses on computational fluid dynamics, fluid-structure interaction, high-performance computing, cardiovascular flows, and bio-inspired propulsion. Professor Borazjani has received prestigious recognitions including AIAA Associate Fellow (2021), ASME Fellow (2020), NSF CAREER Award (2015), and Fulbright Scholarship (2017-2018). He directs the Scientific Computing and Biofluids Lab, developing advanced simulation tools for complex fluid-structure interaction problems. His recent work demonstrates three primary thrusts: 1) Cardiovascular engineering with focus on heart valve dynamics and blood coagulation; 2) Bio-inspired propulsion systems using high-fidelity simulations of fish swimming; and 3) Active flow control techniques including traveling wave morphing surfaces for aerodynamic applications. His group develops specialized computational methods including curvilinear immersed boundary techniques, parallel large eddy simulations, and machine learning approaches for inverse problems in biomechanics.

Arun Srinivasaمشاهده پروفایل

استاد

• Fracture Mechanics
• Computational Mechanics
• Materials Science
• +۷ مورد دیگر

Arun Srinivasa is a Professor in the Department of Mechanical Engineering and Associate Dean for Student Success at Texas A&M University, holding the J.N. Reddy Chair in Applied Mechanics. He is affiliated with the Dwight Look College of Engineering and leads research in fracture mechanics, smart materials, computational modeling, and manufacturing processes. His work includes developing nonlocal fracture criteria, graph-based finite element analysis (GraFEA), and innovations in engineering education. Education: Ph.D. in Mechanical Engineering from UC Berkeley (1991), B.Tech. from Indian Institute of Technology (1986). Research focuses on fracture mechanics of metals, polymers, and biomaterials; simulation of manufacturing techniques like magnetic polishing; and mechanics of 3D printing and architectured materials. He also explores educational advancements, including AI-driven automated grading and interdisciplinary STEM pedagogy. Publications emphasize computational mechanics, fracture modeling, and educational innovations. Notable trends include nonlocal damage models, GraFEA applications, and leveraging LLMs for assessment. 2022 ASME Ben C. Sparks Medal 2018 Archie Higdon Distinguished Educator Award 2017 SES Fellow Advises on curriculum development and cybermanufacturing initiatives. Leads the TCORPS project to enhance student preparation. Active in lab research and educational outreach programs linking K-12 to university engineering. Hosts the Srinivasa Research Lab, focusing on computational mechanics and educational technology. Collaborates on projects like magnetic-abrasive finishing and bone scaffold topology optimization.

John Keyserمشاهده پروفایل

استاد

• Computer Science
• Graphics and visualization
• Physically-based simulation
• +۵ مورد دیگر

John Keyser is a Professor in the Department of Computer Science & Engineering at Texas A&M University, serving as Graduate Advisor. He holds a Ph.D. from the University of North Carolina at Chapel Hill and multiple B.S. degrees from Abilene Christian University. His research focuses on geometric computing, physically-based simulation, graphics, and modeling, with notable contributions to 3D visualization and computational geometry. Keyser's educational background includes advanced degrees in Computer Science, Applied Mathematics, and Engineering Physics. He has received prestigious awards such as the William Keeler Faculty Fellowship and teaching accolades from the Association of Former Students and Tenneco. His research interests span robust geometric modeling, physically-based simulation, brain networks, and graphics. Keyser's work bridges theoretical foundations with applied systems, including innovations in particle-based fluids, mesh rendering, and filament tracking in microscopy. Awards: William Keeler Faculty Fellow (2009-2010), Teaching Excellence Awards (2007), Montague Scholar (2003-2004). Advising: Graduate Advisor role with expertise in guiding students through complex computational projects. Keyser collaborates on advanced imaging techniques like the Knife-Edge Scanning Microscope (KESM) for brain tissue analysis and contributes to GPU-accelerated algorithms for real-time simulations. His research trends emphasize interdisciplinary applications of geometric computing in both academic and industrial contexts.

Eike Muellerمشاهده پروفایل

دانشیار

• Numerical Analysis
• Parallel Computing
• Partial Differential Equations
• +۲ مورد دیگر

Dr. Eike Mueller is a Reader (Associate Professor) in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing fast numerical algorithms for solving physical problems across scales, from atmospheric models to subatomic particles. As part of the Numerical Analysis group, he specializes in parallel computing implementations using frameworks like DUNE and Firedrake/PyOP2. His work is characterized by close collaboration with the Met Office on weather and climate forecasting models. Research interests center on numerical techniques for complex systems, emphasizing parallel computing architectures including GPU clusters. Key areas include massively parallel solvers for PDEs, multilevel Monte Carlo methods for atmospheric dispersion, and performance-portable frameworks for physics simulations. Recent work explores machine learning integration with traditional numerical methods for enhanced computational efficiency. Publications demonstrate consistent focus on accelerating scientific computation through novel algorithms. Recent articles emphasize the application of multigrid methods to climate modeling, neural networks in numerical integration, and Bayesian inference techniques. A significant portion of work targets performance optimization on modern hardware architectures including GPU acceleration. Research leadership includes principal investigator roles for major EPSRC projects: 'MGHyPE: An ExaHyPE version with a multigrid solver' and 'IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers'. Collaboration with the Met Office has resulted in operational improvements to weather prediction models through bespoke solver development.

Mike Adamsمشاهده پروفایل

استاد

• Process Engineering
• Tribology
• Particle Technology
• +۷ مورد دیگر

Professor Mike Adams is a Professor of Product Engineering and Manufacturing at the University of Birmingham's School of Chemical Engineering. His research focuses on process engineering, tribology, particle technology, sensory perception, and computational modelling. He has authored over 230 papers and 5 edited books, and coordinates major grants from the EU and EPSRC. Key achievements include the Donald Julius Groen Prize and Tribology Trust Silver Medal. Education: PhD in Molecular Acoustics from the University of Essex. Professional Fellowships include Royal Academy of Engineering, Institution of Chemical Engineers, Institute of Physics, and Royal Society of Chemistry. He leads editorial roles in seven scientific journals, including Surface Topography: Metrology and Properties and Biosurface and Biotribology . Research interests span interfacial engineering, tribology applications in powder technology and skin friction, molecular dynamics, and discrete element methods. Current projects include pressure ulcer prevention, hydrocolloid dispersion, polymer plasticisation, and haptic feedback in VR environments. His work bridges material science and biomedical engineering, with contributions to tactile perception and medical device design. Awards and recognitions include sustained contributions to tribology research and interfacial engineering. Grants managed total over £multi-million, emphasizing industrial and EU partnerships. Advises postgraduate students in Engineering Doctorate schemes and EU projects, focusing on translational research with industry impact. Labs and teams: Active in the University of Birmingham's Centre for Mathematical Modelling and Chemical Engineering, contributing to low-cost SOFC stack development and mass manufacturing initiatives.

Tonatiuh Sanchez-Vizuetمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• Computational Physics
• +۶ مورد دیگر

Tonatiuh Sánchez-Vizuet is an Assistant Professor in the Department of Mathematics at the University of Arizona , where he develops numerical methods for magnetically confined plasma simulations and computational physics problems. Education B.Sc. in Physics, National Autonomous University of Mexico (UNAM) M.Sc. in Mathematics, National Autonomous University of Mexico (UNAM) Ph.D. in Applied Mathematics, University of Delaware Research Interests center on numerical analysis, scientific computing, and computational physics, with specific focus areas including: Hybridizable Discontinuous Galerkin (HDG) methods Boundary Element Method (BEM) for time-domain wave propagation Coupling integral and partial differential equations Uncertainty quantification in plasma physics Transient electromagnetic scattering analysis Thermo-piezoelectric wave interactions Scientific Contributions include 15+ peer-reviewed publications on HDG discretizations, boundary integral formulations, and surrogate modeling. His work spans applications in: Magnetic confinement fusion reactors Wave-structure interaction Transient thermoelasticity Dielectric scattering problems Grants NSF-DMS-2137305 (2021-2023): $250,000 as single PI for HDG methods in plasma confinement Awards Featured by Lathisms.org as prominent Hispanic mathematician (2018) Teaching includes graduate-level real analysis and mathematical statistics courses, with class notes freely available for student use.

Jaroslaw Krzywanskiمشاهده پروفایل

استاد

• CFD Modeling
• Fluidization
• Fuzzy Logic
• +۱۱ مورد دیگر

Prof. Jaroslaw Krzywanski serves as a Professor in the Department of Advanced Computational Methods at Jan Dlugosz University of Czestochowa, Poland. His academic profile centers on advanced computational modeling for energy systems and environmental engineering, with extensive publications in high-impact thermal and energy journals. His research spans CFD Modeling, Fluidization, Fuzzy Logic, and Artificial Intelligence applications in combustion, emissions control, and thermal processes. Key focus areas include Chemical Looping Combustion, Adsorption Cooling Systems, and NOx/SO2 Emission Reduction , leveraging computational methods to optimize energy conversion and pollution mitigation. Recent work integrates Deep Learning and AutoML frameworks with traditional engineering models for predictive analysis in fluidized reactors and desalination systems. Analysis of his 2024-2025 publications reveals a dominant trend toward hybrid computational approaches —coupling CFD-DEM simulations with AI techniques to address heat transfer challenges in adsorption reactors, plastic waste co-incineration, and vacuum distillation. Over 70% of recent work emphasizes sustainable energy solutions through waste-to-energy conversion and cleaner combustion technologies, demonstrating consistent focus on ecological system design.

• Rock Mechanics
• Rock Dynamics
• Underground Construction
• +۳ مورد دیگر

Professor Jian Zhao is a faculty member in the Department of Civil & Environmental Engineering at Monash University, specializing in Rock Mechanics, Geophysics, and Geothermal Energy. He holds editorial roles in journals such as the International Journal of Rock Mechanics and Mining Sciences and has led significant research projects funded by organizations like the Australian Research Council. His work focuses on rock dynamics, underground construction, and advanced material testing techniques. Notable achievements include developing novel methods for rock fracturing via microwave treatment and pioneering numerical models for rock fracture under dynamic loads. Professor Zhao is an ISRM Fellow and has conducted collaborative research with global institutions. His projects address challenges in geothermal energy extraction and tunneling safety, with a strong emphasis on sustainability and engineering innovation. Research interests span across Dynamic rock behavior under multiaxial loading, Microwave-enhanced rock breakage, Fracture mechanics in heterogeneous rock formations, Advanced numerical modeling (FDEM, DEM), Geothermal energy applications, Seismic response of rock joints. Recent studies include innovations in automated excavation frameworks, thermal-stress-pore pressure coupled testing systems, and insights into induced seismicity through photoelastic techniques. Over 373 publications and active grants reflect his impactful contributions to civil and environmental engineering.

Euan Spenceمشاهده پروفایل

استاد

• Wave Propagation
• Helmholtz Equation
• Numerical Analysis
• +۶ مورد دیگر

Euan Spence is a Professor of Mathematics at the University of Bath, part of the Department of Mathematical Sciences and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on the numerical analysis and mathematical analysis of wave propagation problems, particularly involving the Helmholtz equation, Maxwell equations, and boundary integral equations. He holds a PhD from the University of Cambridge (2009) and has held academic positions at Bath since 2009, advancing to Professor in 2020. His work bridges semiclassical analysis and numerical methods, addressing challenges in high-frequency wave scattering and uncertainty quantification. Spence has led or contributed to multiple research projects funded by EPSRC and EU, including the ERC Synergy Grant for phase-space-inspired numerical methods. His awards include the London Mathematical Society Whitehead Prize (2022) and the John Todd Award (2019). He supervises PhD students and postdocs, contributing to areas like domain decomposition methods, finite element analysis, and seismic imaging optimization. His educational background includes a Master’s and PhD from the University of Cambridge, specializing in transform methods for linear PDEs under Thanasis Fokas. He teaches courses on partial differential equations and numerical analysis, emphasizing rigorous mathematical foundations and practical computational skills.

Xilin Xiaمشاهده پروفایل

استادیار

• Computational Hydraulics
• High-Performance Computing
• Machine Learning Applications
• +۴ مورد دیگر

Dr. Xilin Xia is an Assistant Professor in Resilience Engineering at the University of Birmingham's School of Engineering. He specializes in computational modeling of natural hazards and their impacts, focusing on flood risk management and infrastructure resilience. His research integrates high-performance computing, machine learning, and hydraulic/hydrological modeling. Education: BEng in Civil Engineering, Wuhan University (2010) MEng in Road and Railway Engineering, Wuhan University (2012) PhD in Water Resources, Newcastle University (2017) Research Interests: Development of open-source tools like HiPIMS for flood modeling Risk-based flood forecasting systems (e.g., FHIM-India project) GPU-based parallel computing for environmental applications Machine learning for hazard risk analytics Funding & Awards: Over £1M in grants as PI/Co-I from UKRI, Met Office, NERC Fellow of the Higher Education Academy (2020) Teaching: Leads 'Water Engineering and Management' module at University of Birmingham. Software Contributions: HiPIMS: High-Performance Integrated hydrodynamic Modelling System PyPIMS: Python interface for HiPIMS

• Granular Materials
• Pattern Formation
• Numerical Simulations
• +۶ مورد دیگر

Antoine Dop is a Research Fellow at the Porous Media Laboratory (PoreLab), University of Oslo. He specializes in experimental and computational physics, focusing on complex granular systems. His affiliations include ongoing research projects at PoreLab, where he resumed work on frictional fingers and magnetic grain dynamics after joining in October 2024. Research Interests: Dop investigates pattern formation mechanisms in granular media using discrete element method (DEM) simulations and lab experiments. His work spans auxetic metamaterials, magnetic/non-magnetic grain segregation, and surface instabilities in granular beds. Key methodologies include: Discrete element method simulations Laboratory-scale granular flow experiments Analysis of frictional instabilities Magnetic particle aggregation dynamics He collaborates internationally, including projects with Brazilian researchers on inclined-plane granular flows. Laboratory & Team: As a member of PoreLab, Dop contributes to interdisciplinary research on porous media physics. His team focuses on experimental and numerical approaches to fundamental granular behavior.

Yupeng Jiangمشاهده پروفایل

استاد

• Computational Mechanics
• Geotechnical Engineering
• Granular Dynamics
• +۶ مورد دیگر

Prof. Dr. Yupeng Jiang is a faculty member at Leibniz University Hannover, holding the position of Professor in the Department of Structural Mechanics and Computational Mechanics within the Faculty of Civil Engineering and Geodesy. His research focuses on computational mechanics, geotechnical engineering, granular dynamics, and fracture mechanics, with notable contributions to numerical simulation techniques and their applications in soil mechanics, particle fragmentation, and environmental engineering. He is affiliated with the Institute of Structural Mechanics and Computational Mechanics and can be contacted at yupeng.jiang@ibnm.uni-hannover.de . His recent work emphasizes hybrid numerical methods (e.g., MP-DEM, coupled material-point approaches) for simulating granular flows, soil-structure interactions, and fracture mechanisms. Key investigations include erosion dynamics in geophysical flows, the role of forests in mitigating flow mobility, and microseismic signal processing for subsurface fracture analysis. His studies often bridge experimental and numerical approaches to address challenges in geotechnical and environmental systems. No scientific awards or notable grants are explicitly mentioned in the provided texts. His advising record is currently unspecified. Prof. Jiang’s laboratory and team are part of the Institute of Structural Mechanics and Computational Mechanics, focusing on advancing computational tools for complex material behavior and geotechnical applications.

Sven Großمشاهده پروفایل

پژوهشگر

• Computational Fluid Dynamics
• Numerical Analysis
• Finite Element Methods
• +۳ مورد دیگر

Dr. Sven Groß is a researcher at the Institute for Geometry and Practical Mathematics at RWTH Aachen University, Germany, with an office in Hauptgebäude 237. His work focuses on advanced numerical methods for fluid dynamics, particularly incompressible two-phase flows, and he is a core developer of the DROPS software package for parallel flow simulations. He maintains active research collaborations across computational mathematics and chemical engineering disciplines. His educational background includes a diploma thesis (2002) and doctoral thesis (2008), both completed at RWTH Aachen University. The doctoral work, titled "Numerical methods for three-dimensional incompressible two-phase flow problems," earned him the prestigious Borchers-Plakette award. Groß's research centers on Computational Fluid Dynamics with emphasis on Finite Element Methods for interface problems. His expertise spans adaptive 3D FE techniques, level set methods, XFEM implementations, and parallelization strategies for two-phase flow systems. Current investigations address preconditioning for unfitted finite element methods, mass transfer in falling films, and high-performance computing approaches for gas absorption processes. His methodologies bridge theoretical numerical analysis with industrial applications in chemical process engineering. Analysis of his recent publications reveals a strong trajectory toward robust preconditioning techniques for interface problems and high-fidelity simulations of reactive multiphase systems. The work increasingly integrates high-throughput computing with traditional numerical methods, demonstrating growing emphasis on computational efficiency for complex industrial-scale problems while maintaining mathematical rigor in error analysis. His scientific recognition includes: Borchers-Plakette (RWTH Aachen, 2009) for doctoral studies Friedrich-Wilhelm-Preis (RWTH Aachen, 2003) for diploma thesis Springorum-Denkmünze (RWTH Aachen, 2003) for diploma Groß has secured significant research funding through Collaborative Research Center SFB 540, leading Project C7 on numerical methods for wavy falling film simulations and contributing to former Project C6 on parameter estimation. He actively mentors students through research collaborations and has supervised numerous diploma/PhD projects, with co-authors including Kirchhart, Ludescher, and Jankuhn appearing consistently in his publication record. As a principal investigator for the DROPS project, he leads a cross-disciplinary team developing parallel adaptive multigrid techniques for incompressible flow simulations. The project maintains strong industry connections with chemical engineering groups focused on falling film reactors and mass transfer optimization, with recent extensions into high-performance computing architectures for industrial-scale simulations.

Tamar Shinarمشاهده پروفایل

دانشیار

• Physics-Based Animation
• Fluid-Structure Interaction
• Biological Cell Modeling
• +۳ مورد دیگر

Tamar Shinar is an Associate Professor in the Computer Science & Engineering Department at the University of California - Riverside (UCR). She is also affiliated with the Interdisciplinary Center for Quantitative Modeling in Biology. Her research focuses on physics-based animation, fluid-structure interaction, and computational modeling of biological systems. She teaches courses such as Scientific Computing, Computer Graphics, and Physics-Based Simulation, demonstrating expertise in both theoretical and applied aspects of computational science. Shinar's work bridges computer graphics and computational biology, with notable contributions to fluid dynamics simulations, deformable body modeling, and microtubule-based motion in biological systems. Her publications span topics like implicit surface reconstruction using neural networks, conservative discretization methods, and partitioned fluid-solid coupling algorithms. She actively mentors PhD and MS students and engages in outreach programs to promote STEM education.