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Xu JinchaoView profile

Professor

• Multigrid methods
• Machine learning
• Finite element methods
• +5 more

Xu Jinchao is a Professor of Applied Mathematics and Computational Sciences at King Abdullah University of Science and Technology (KAUST) and the Verne M. Willaman Professor of Mathematics at Penn State University. He has held distinguished roles, including Director of the Center for Computational Mathematics and Applications at Penn State since 1997 and is an Affiliated Faculty member of the College of Information Sciences and Technology at Penn State. His research focuses on numerical partial differential equations (PDEs), multigrid methods, machine learning, finite element methods, and domain decomposition methods. He is renowned for pioneering contributions such as the Bramble-Pasciak-Xu (BPX) preconditioner, Hiptmair-Xu (HX) preconditioner, Xu-Zikatanov (XZ) identity, and Morley-Wang-Xu (MWX) element. His work bridges computational mathematics and machine learning, including the development of MgNet, which unifies multigrid methods with convolutional neural networks. Xu has been recognized with numerous awards, including Fellowships from SIAM, AMS, AAAS, and the European Academy of Sciences. Notable accolades include the 2008 DOE Top 10 Breakthroughs for his HX preconditioner and the 1995 Feng Kang Prize for Scientific Computing. He has organized over 100 conferences and serves on editorial boards of top journals such as Mathematics of Computations and Numerische Mathematik . His leadership includes directing research centers and advancing computational science through collaborative efforts.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Method
• +3 more

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.

Paula HilbertView profile

Researcher

• Numerics of PDEs
• Adaptive FEM
• Contractive Iterative Solvers
• +5 more

Paula Hilbert is a PhD student and University Assistant at TU Wien's Institute of Analysis and Scientific Computing, supervised by Prof. Dirk Praetorius. Her research focuses on Numerics of PDEs, adaptive FEM, and contractive iterative solvers, with current emphases on Finite Element Methods, Multigrid methods, and optimal preconditioners for Galerkin matrices. She teaches the course 'Introduction to Programming' (4 VU) in the summer term of 2025. Education: BSc (1998) and MSc (2022) in Technical Mathematics from TU Wien. Current affiliation includes the ASC-Nextcloud and TU-Webmail platforms. No awards or grants are explicitly listed in the provided materials.

Ulrich LangerView profile

Professor

• Numerical Methods for PDEs
• Computational Mechanics
• Computational Electrodynamics
• +6 more

Ulrich Langer is a Full Professor of Numerical Mathematics at Johannes Kepler University Linz (JKU), Austria. He has held academic positions since 1980, including roles as Scientific Director of the Radon Institute for Computational and Applied Mathematics (2003–2020) and leader of its PDE computational methods research group. He specializes in numerical methods for PDEs, computational mechanics, and parallel computing. Education: Born 1952 in Germany, studied Mathematics at Technical University Karl-Marx-Stadt (Diploma 1975) PhD in Numerical Mathematics from Leningrad University (1980) Habilitation and professorships at Technical Universities of Chemnitz and Linz His research focuses on domain decomposition methods, multigrid techniques, and isogeometric analysis. Key contributions include projects funded by FWF and NSF, such as 'Discontinuous Galerkin Domain Decomposition Methods in Isogeometric Analysis' and 'Goal-Oriented Error Control for Phase-Field Fracture.' Professional Roles: Editorial Board Member of journals like Computing, SIAM Journal on Numerical Analysis, and ZAMM ERC Panel Member for Mathematics (2008–2025) Organized international conferences, e.g., 17th Domain Decomposition Methods (2006) Awards: Multi-Grid Prize (2025). Extensive supervision of 29 PhD students and 24 diploma theses.

Michael InnerbergerView profile

Researcher

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.

Daniel JodlbauerView profile

Researcher

• Computational Mathematics
• Numerical Analysis
• Biomedical Imaging
• +4 more

Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.

Michael ReicheltView profile

Researcher

• Finite Element Analysis
• Electric Machines
• Hydrodynamics
• +5 more

Michael Reichelt is a researcher affiliated with the Institute of Applied Mathematics , focusing on computational methods and simulations in engineering and physics. His work spans finite element analysis, multigrid techniques, and space-time modeling for complex systems. Research interests include: Fundamental and applied numerical mathematics Space-time finite element methods for parabolic equations Contact simulations in elasto-hydrodynamic systems Isogeometric analysis for electric machine modeling Magnetohydrodynamic stability in tokamaks Recent publications highlight advancements in optimal control problems, multigrid efficiency, and transient simulations using IGA. His activities involve presenting at international conferences on computational techniques.

Gundolf HaaseView profile

Professor

• Scientific Computing
• High Performance Computing
• Parallel Algorithms
• +7 more

Gundolf Haase is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz, Austria. His academic career spans several decades with significant contributions to parallel computing and numerical methods for partial differential equations. He maintains an active research profile with numerous publications in high-impact journals and conferences. His research interests primarily focus on Scientific Computing, High Performance Computing, Parallel Algorithms, Domain Decomposition Methods, Finite Element Methods, Multigrid Methods, and GPU Programming. His work has increasingly incorporated biomedical applications, particularly in cardiac electrophysiology modeling and computational fluid dynamics for cardiovascular applications. Haase's recent publications demonstrate a strong focus on many-core parallelization techniques, particularly for GPUs, with applications spanning from computational cardiology to engineering simulations. His research shows a consistent pattern of developing efficient numerical methods for solving complex partial differential equations across various scientific domains. He teaches courses including Scientific Computing and FEM, Computer Mathematics, Programming in C++, and High Performance Computing at both undergraduate and graduate levels, demonstrating his commitment to education alongside research.