جست‌وجوی استاد و پژوهشگر

• Numerical Methods
• Finite Element Methods
• Fluid Dynamics
• +۹ مورد دیگر

Douglas R. Q. Pacheco is a Junior Principal Investigator (JPI Fellow) and Junior Research Group Leader at RWTH Aachen University, jointly affiliated with the Chair for Computational Analysis of Technical Systems (CATS) and MBD within the Faculty of Mechanical Engineering. He leads a research group focused on advanced numerical methods for simulating complex fluid flow problems. Institution: RWTH Aachen University School: Faculty of Mechanical Engineering Department: Chair for Computational Analysis of Technical Systems (CATS) Position: Junior Research Group Leader, JPI Fellow Email: pacheco@ssd.rwth-aachen.de Education: PhD in Technical Mathematics, TU Graz, Austria (2021) MSc in Mechanical Engineering, University of São Paulo (USP), Brazil (2018) Diploma in Aeronautical Engineering, University of São Paulo (USP), Brazil (2017) Dr. Pacheco's research centers on the development and application of advanced numerical methods for simulating complex fluid dynamics, particularly involving non-Newtonian fluids. His work spans finite element methods (FEM) , implicit-explicit (IMEX) schemes , and simulations of granular flows , landslides , multi-phase flows , and viscoelastic and non-Newtonian hemodynamics . His expertise lies at the intersection of computational mechanics and applied mathematics, aiming to improve the efficiency and accuracy of simulations in engineering and biomedical contexts. Although no recent publications are explicitly listed in the provided text, his research direction suggests strong engagement in high-impact computational fluid dynamics, particularly in geophysical and biological flows. His work likely involves collaborations across disciplines, including mechanical engineering, applied mathematics, and biomechanics. Scientific Awards and Honors: No awards explicitly mentioned. Dr. Pacheco is actively involved in research supervision and collaboration, welcoming inquiries from potential students and partners. He previously held postdoctoral positions at NTNU (Norway) and TU Graz (Austria), building a strong international research profile. He leads an independent junior research group, indicating active grant funding and leadership in his field. Laboratories and Research Groups: Junior Research Group in Computational Analysis of Technical Systems, RWTH Aachen Collaboration with CATS and MBD Chairs

Sigrun Ortlebمشاهده پروفایل

دانشیار

• Discontinuous Galerkin methods
• Numerical methods for fluid-structure interaction
• Positivity-preserving time integration
• +۴ مورد دیگر

PD Dr. Sigrun Ortleb is an Associate Professor at the Institute of Mathematics, University of Kassel , Germany. Her academic affiliation spans over two decades at the same institution. Current Position: Associate Professor (Privatdozentin) since 2021 Academic Background: Habilitation (2021), PhD (2011), Diplom (2006) from TU Braunschweig Research Focus: Sigrun Ortleb specializes in advanced numerical methods for partial differential equations, with particular emphasis on: Discontinuous Galerkin (DG) methods for conservation/balance equations (Euler, Navier-Stokes, shallow water) Positivity-preserving time integration techniques Summation-by-parts operators for high-order accuracy IMEX and multi-rate time integration Efficient shock filters for DG processes Image processing methods for numerical solution post-processing Publication Trends: Her work primarily addresses computational fluid dynamics challenges through: Development of stable, high-order DG schemes IMEX time integration for stiff systems Geometric conservation laws in FSI Adaptive filtering techniques Positivity preservation in shallow water equations Summation-by-parts operators for conservation Teaching Contributions: Dr. Ortleb has taught advanced mathematics courses for mechanical engineers since 2007, including: Higher Mathematics I & II Numerics of Stiff Problems Fluid-Structure Interaction Numerical Analysis of ODEs/PDEs Mathematical Modeling Discontinuous Galerkin Methods Key Collaborations: She has collaborated with experts in: Computational mechanics (J. Boungard, J. Wackerfuß) Exponential integrators (V. Straub, P. Birken, A. Meister) Geometric conservation laws (S. Bremicker-Trübelhorn)

• Numerical Analysis
• Partial Differential Equations
• Fluid Mechanics
• +۹ مورد دیگر

Pep Mulet Mestre is a full Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València. He is a member of the ANIMS research group, focusing on Numerical Analysis, Images, Multiresolution, and Simulation. University: Universitat de València School: Faculty of Mathematics Department: Department of Mathematics Research Group: ANIMS His primary research interests include numerical analysis, partial differential equations, fluid mechanics, image processing, and high-order finite difference methods. He has made significant contributions to WENO schemes, total variation-based image restoration, and models for sedimentation and traffic flow. The 15 most recent publications highlight a consistent focus on high-order accurate numerical methods, particularly WENO and IMEX schemes, applied to conservation laws, image processing, and biological models. His work bridges theoretical numerical analysis with practical applications in fluid dynamics and epidemiology. He has collaborated extensively with researchers such as Raimund Bürger, Rosa Donat, Antonio Baeza, and David Zorío, resulting in over 60 indexed publications since 1996. Dr. Mulet earned his PhD from the Universitat de València in 1992 with a thesis on local cohomology and duality in non-commutative Gorenstein rings, supervised by Dr. A. Verschoren. He advises graduate students and leads research projects in numerical methods for PDEs and scientific computing, though specific advisees are not listed in the provided text. There is no mention of specific grants or scientific awards in the available information.

Arash Sarsharمشاهده پروفایل

استادیار

• Machine Learning
• Numerical Methods for Scientific Simulations
• Data Science for Public Health and Policy
• +۳ مورد دیگر

Arash Sarshar is an Assistant Professor at the Computer Engineering and Computer Science Department of California State University, Long Beach. Previously, he served as a postdoctoral associate in the Department of Computer Science at Virginia Tech, where he also earned his Ph.D. His academic background includes Master's and Bachelor's degrees in Electrical Engineering. Education : Ph.D. in Computer Science (Virginia Tech), M.S. and B.S. in Electrical Engineering. Sarshar's research focuses on the intersection of machine learning and numerical methods for scientific simulations. He explores physics-informed neural networks, deep operator learning, and time-stepping strategies to enhance computational models, particularly for partial differential equations (PDEs) and stiff systems. His work also extends to applying data science techniques for public health and policy analysis, including studies on diabetes lifestyle interventions. His recent publications highlight trends in PDE-constrained optimization, Bayesian parameter estimation, multirate integration schemes, and parallel computing for numerical methods. Key topics include physics-informed machine learning, time-stepping algorithms, and computational modeling for multiphysics problems.

• Mathematical Physics
• Fluid Dynamics
• Kinetic Theory
• +۳ مورد دیگر

Giorgio Martalò is a Researcher in the Department of Mathematical Physics at the University of Pavia. His work bridges mathematical physics, fluid dynamics, and applied mathematics, focusing on kinetic theory applications to gaseous systems, biological phenomena, and environmental engineering. Key research areas include chemotaxis modeling for Multiple Sclerosis, shock wave dynamics in gas mixtures, and optimal control strategies for waste treatment processes. His research explores both theoretical advancements and practical applications, such as deriving hydrodynamic limits from kinetic equations, analyzing reaction-diffusion systems with Allee effects, and developing numerical methods for complex fluid flows. Recent work addresses misinformation dynamics through prebunking strategies and investigates non-equilibrium phenomena in multi-component gas mixtures. Publications span multiscale modeling techniques, including hybrid kinetic models and IMEX finite volume schemes, alongside studies on thermal non-equilibrium in shock waves and optimal control of biocell composting systems. His interdisciplinary approach integrates mathematical rigor with real-world problem-solving in biomedical, environmental, and engineering domains.

Thomas P. Witelskiمشاهده پروفایل

استاد

• Fluid Dynamics
• Partial Differential Equations
• Nonlinear Dynamical Systems
• +۴ مورد دیگر

Thomas P. Witelski is a Professor at Duke University with joint appointments in the Department of Mathematics (Trinity College of Arts & Sciences, since 2011) and Thomas Lord Department of Mechanical Engineering and Materials Science (Pratt School of Engineering, since 2016). His work focuses on applied mathematics and industrial fluid dynamics. Ph.D. in Applied Mathematics (Caltech, 1995) B.S.E. in Engineering (Cooper Union, 1991) Research Focus Specializing in nonlinear differential equations , Witelski develops mathematical models for viscous fluid flow , dewetting thin films , and dynamical systems . His research extends to industrial applications like spin coating and biomedical contexts such as tear film dynamics in dry-eye syndrome. Publication Trends Recent work (2024-2025) addresses IMEX numerical methods for thin films, coarsening dynamics , and tumor growth instabilities . Earlier studies (2018-2020) explore self-similar solutions , Marangoni effects , and heterogeneous substrate interactions . Awards & Service Awarded the NSF CAREER Award (2003), Witelski organizes conferences like the TriCAMS Symposium (2023) and Mathematical Problems in Industry Workshop (2016). He mentors graduate and undergraduate students in mathematical modeling camps and serves on editorial boards for dynamical systems journals.

• Numerical Analysis
• Finite Element Methods
• Fluid Mechanics
• +۶ مورد دیگر

Gabriel Barrenechea is a Reader in the Department of Mathematics and Statistics at the University of Strathclyde, Faculty of Science. His academic work centers on the development and analysis of finite element methods with strong theoretical foundations, particularly applied to fluid mechanics and partial differential equations. His research interests lie at the intersection of numerical analysis and computational mathematics, with a focus on ensuring stability, accuracy, and physical consistency in simulations. Key areas include discrete maximum principles, bound-preserving schemes, stabilization techniques for convection-dominated flows, and multiscale methods. His work often addresses challenges in non-Newtonian fluids and high-Weissenberg number problems. The recent publications reflect a sustained focus on finite element discretizations that preserve physical bounds and mathematical monotonicity. These works span elliptic, parabolic, and fluid flow problems, employing hybrid, nodal, and implicit-explicit formulations. The trend shows increasing sophistication in handling nonlinearities, variable coefficients, and complex physical constraints through mathematically rigorous approaches. Best paper award Sociedad Espanola de Matematica Aplicada (SEMA) 2018 Gabriel Barrenechea has been actively involved in research leadership and student training. He has served as Principal Investigator on multiple EPSRC-funded and institutional projects, including doctoral training partnerships. He supervises postgraduate research and contributes to academic development frameworks. His professional activities include organizing major conferences such as the Biennial Conference on Numerical Analysis and the Scottish PDE Colloquium. He is a key organizer of academic events including the Impact Case Studies in Maths & Stats workshop and the Early Career Network in Mathematics and Statistics. His collaborations span the UK and international institutions, reflected in co-authored works and joint projects.

Andrey A. Popovمشاهده پروفایل

استادیار

• Computational Science
• Data Assimilation
• Bayesian Inverse Problems
• +۸ مورد دیگر

Andrey A. Popov is an Assistant Professor in the Department of Information and Computer Sciences at the University of Hawaiʻi at Mānoa, part of the College of Natural Sciences. His research lies at the intersection of computational science, data assimilation, uncertainty quantification, and machine learning, with applications to dynamical systems and aerospace navigation. His research interests include computational science, Bayesian inverse problems, data assimilation, uncertainty quantification, theory-guided machine learning, reduced order modeling, and ensemble filtering. He develops methods that bridge theoretical rigor with practical applicability, aiming for solutions no more than one step away from real-world implementation. His work often integrates machine learning with physical models to improve state estimation and prediction in complex systems. The recent publications (2023–2024) reflect a strong trend in advancing ensemble-based filtering techniques—particularly ensemble Kalman and particle filters—with innovations in covariance adaptation, weight optimization, and non-Gaussian modeling. There is also a growing emphasis on multifidelity and reduced-order modeling, especially using autoencoders and neural networks to handle small data regimes in chaotic systems. Applications span geophysics, aerospace (e.g., Mars entry navigation), and cislunar tracking, demonstrating interdisciplinary impact. Scientific Awards: Jean-Pierre Le Cadre Best Paper Award at FUSION 2024 Dr. Popov actively mentors students and welcomes prospective graduate students to contact him via email or during office hours. While specific grant details are not provided, his publication output and conference presence suggest active external funding. He teaches courses such as ICS 141 and emphasizes algorithmic and mathematical foundations in computing. He is involved in collaborative research with institutions like the Oden Institute and researchers including Adrian Sandu and Renato Zanetti, focusing on advanced filtering and data assimilation techniques. His group likely engages in methodological development for state estimation in high-dimensional, nonlinear systems.

Chen Liuمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Fluid Dynamics
• +۲ مورد دیگر

Chen Liu is an Assistant Professor of Mathematics in the Department of Mathematical Sciences at the University of Arkansas's Fulbright College of Arts and Sciences. He holds a PhD in Computational and Applied Mathematics from Rice University. Research Interests: Dr. Liu's research focuses on developing advanced numerical methods for partial differential equations, particularly discontinuous Galerkin schemes for fluid dynamics applications. His work emphasizes structure-preserving algorithms for hyperbolic conservation laws, Navier-Stokes equations, and multiphase flow problems. Key research areas include bound-preserving limiters, high-order accuracy schemes, and efficient solvers for complex physical systems. Publication Trends: His recent publications demonstrate a consistent focus on improving numerical stability and accuracy for challenging fluid dynamics problems. Common themes include positivity-preserving schemes, multiphysics coupling (particularly Cahn-Hilliard-Navier-Stokes systems), and applications to porous media flow. Recent work shows increased emphasis on optimization-based approaches and implicit-explicit time integration techniques. Awards: No scientific awards mentioned. Academic Activities: No information available regarding students advised, research grants, or laboratory facilities.

• Nonlinear Partial Differential Equations
• Numerical Analysis
• Applied Computational Mathematics
• +۳ مورد دیگر

Tori Fisher is a doctoral student and Graduate Research Assistant in the School of Mathematical and Statistical Sciences at Clemson University. She holds a B.S. in Mathematical Sciences from Clemson (2023) and focuses on applied and computational mathematics with emphases on nonlinear PDE analysis, numerical methods, and coding theory. Education: B.S., Mathematical Sciences, Clemson University, 2023 Her research interests include nonlinear partial differential equations , nonlinear solvers , Anderson Acceleration , and CSS-T code construction from Reed Muller codes . She has presented work at NCUWM 2023 and the AMS Southeast Sectional 2023 conferences. Notable achievements include the Samuel M. Martin Outstanding Senior Award (2023) and the Michael Case Award for promising PhD research (2024). She is expected to graduate in May 2028.

Raymond J. Spiteriمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۵ مورد دیگر

Raymond J. Spiteri is a Professor in the Department of Computer Science at the University of Saskatchewan, where he also directs the Centre for High-Performance Computing. His research spans numerical analysis, scientific computing, and applied mathematics, with a focus on developing numerical methods and software for differential equations, computational fluid dynamics, and exascale computing. He has led projects funded by Mprime and Mitacs, collaborating with industries like AFCC and Ballard Power. Education: Ph.D. in Mathematics (Institute of Applied Mathematics), University of British Columbia, 1997 B.Sc. (Hons.) in Applied Mathematics (Theoretical Physics), University of Western Ontario, 1990 Postdoctoral Fellow, School of Computer Science, McGill University, 1997–1999 Research Interests: Numerical methods for ODEs/DAEs/PDEs, including Runge-Kutta and SSP methods High-performance computing and parallel programming Applications in fluidized bed simulation, cardiac electrophysiology, and climate modeling Software development for scientific computing (e.g., problem-solving environments) Recent Article Trends: Advancements in implicit-explicit methods and operator-splitting techniques Computational modeling of cardiac arrhythmias and electrochemical systems Large-scale simulations for environmental and geophysical processes High-performance computing optimizations for exascale architectures Grants & Leadership: Director of the Centre for High-Performance Computing (College of Arts and Science) Executive Committee member of WestGrid/Compute Canada Past leader of the Mprime project on fuel cell simulation and transport phenomena Labs & Teams: Research is conducted in the Numerical Simulation Laboratory, focusing on interdisciplinary projects with industrial and academic partners.

Sigal Gottliebمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Hyperbolic PDEs
• +۲ مورد دیگر

Dr. Sigal Gottlieb is Chancellor Professor of Mathematics at UMass Dartmouth and founding director of the Center for Scientific Computing and Data Science Research. A Harvard Business School PLDA graduate and computational mathematics expert, her research develops high-order numerical methods for simulating hyperbolic PDEs with applications in gravitational wave physics. Research Focus: Specializes in strong stability preserving (SSP) time discretizations, WENO/spectral methods for discontinuous problems, and GPU-accelerated algorithms for computational relativity. Current NSF-funded projects include developing efficient black hole spectroscopy techniques. Leadership: Established UMass Dartmouth's computational science research hub and developed new academic programs including the Data Science BS/MS degrees. Recognized as SIAM and AWM Fellow for contributions to computational mathematics.

Zheng Chenمشاهده پروفایل

دانشیار

• Numerical analysis
• Scientific computing
• High performance computing
• +۸ مورد دیگر

Zheng Chen is an Associate Professor in the Department of Mathematics at the University of Massachusetts Dartmouth. Her research focuses on scientific computing and numerical analysis, with applications to fluid dynamics, kinetic theory, finance, and engineering mechanics. She earned her PhD in Applied Mathematics from Brown University in 2014. Research Interests: Numerical analysis Scientific computing High performance computing Machine learning Image processing Multi-scale computational methods Uncertainty quantification Scientific Awards: $13,000 from MAA for Mixed Model Implicit and IMEX Runge-Kutta Methods $438,284 from ONR for UMassD MUST IV project Education: PhD (2014) and MS (2010) from Brown University

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.

Chi-Wang Shuمشاهده پروفایل

استاد

• Numerical Solutions of Conservation Laws
• Computational Fluid Dynamics (CFD)
• Discontinuous Galerkin Methods
• +۵ مورد دیگر

Chi-Wang Shu is the Stowell University Professor of Applied Mathematics and former Chair of the Division of Applied Mathematics at Brown University. His research focuses on high-order numerical methods for convection-dominated partial differential equations, including ENO/WENO schemes, discontinuous Galerkin methods, and spectral methods. He has contributed extensively to computational fluid dynamics, semiconductor modeling, and Hamilton-Jacobi equations. Shu has held academic positions at Brown since 1987 and has received accolades such as the SIAM/ACM Prize in Computational Science (2007) and SIAM/AMS Fellowships (2009, 2012). His work bridges theoretical development and practical applications in fluid dynamics, astrophysics, and materials science. Education: B.S., University of Science and Technology of China (1982); Ph.D., UCLA (1986). Affiliations: Brown University (1987–present), Chair of Applied Mathematics (1999–2005). Research interests span numerical methods for conservation laws, with emphasis on shock-capturing algorithms and their application to multiphysics problems. Notable contributions include the development of WENO schemes and DG methods for complex flows. He has authored over 400 publications and advised numerous students in computational mathematics and engineering. Key awards: NASA Public Service Group Achievement Award (1992), Feng Kang Prize (1995), ISI Highly Cited Author (2004).