جست‌وجوی استاد و پژوهشگر

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +۷ مورد دیگر

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

Hans-Georg Herzogمشاهده پروفایل

استاد

• Electrical Machines
• Electromechanical Drives
• Energy Conversion Technology
• +۷ مورد دیگر

Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.

Karsten Urbanمشاهده پروفایل

استاد

• Numerical Mathematics
• Scientific Computing
• Partial Differential Equations
• +۹ مورد دیگر

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

Martin Greplمشاهده پروفایل

استاد

• Numerical Methods for Partial Differential Equations (PDEs)
• Model Order Reduction
• Reduced Basis Methods
• +۴ مورد دیگر

Professor Dr. Martin Grepl is a faculty member at RWTH Aachen University, where he holds the Lehr- und Forschungsgebiet Optimierung mit partiellen Differentialgleichungen (Teaching and Research Area in Optimization with Partial Differential Equations). He has been affiliated with RWTH Aachen since 2009, first as a Professor (W1) and since 2014 as a Professor (W2). Education: Diplom-Ingenieur (Aerospace Engineering), University of Stuttgart (2000) Master of Science (Mechanical Engineering), MIT (2001) Doctor of Philosophy (Mechanical Engineering), MIT (2005) His research focuses on numerical methods for partial differential equations (PDEs) , particularly model order reduction , reduced basis methods , finite element methods , and optimal control for parametrized PDEs. He also investigates parameter estimation , inverse problems , and control constraints in elliptic and parabolic PDE systems. The scientific awards he has received include the Studienstiftung des deutschen Volkes (1997-2000), a Fellowship from the Dr. Jürgen Ulderup-Stiftung (1998-1999), and the Lehrpreis der Fachschaft Mathematik/Physik/Informatik (2011). His work spans applications in manufacturing , medical physics , and fluid dynamics , as evidenced by his patents and collaborative research. His publications demonstrate expertise in reduced basis methods for nonaffine/nonlinear PDEs , trust region optimization , and error bounds for real-time and many-query scenarios. His collaborations often involve interdisciplinary applications, including thermal conduction , welding processes , and glomerular filtration modeling .

Tobias Neckelمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +۴ مورد دیگر

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

Benjamin Stammمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• High-Performance Computing (HPC)
• +۷ مورد دیگر

Prof. Benjamin Stamm is a Professor of Numerical Mathematics at the University of Stuttgart, leading the Chair of Numerical Mathematics for High Performance Computing within Faculty 08. He holds a Ph.D. and master's degree in mathematics from École Polytechnique Fédérale de Lausanne (EPFL) and has previously worked at RWTH Aachen University, Sorbonne Université UPMC Paris 6, UC Berkeley, and Brown University. His research focuses on numerical analysis, scientific computing, and simulations, particularly efficient discretizations for PDEs, eigenvalue problems, error certification, reduced basis methods, and HPC implementations. He develops scalable numerical methods for problems in computational chemistry and physics, emphasizing accuracy, efficiency, and interdisciplinary collaboration with chemists, physicists, and materials scientists. Prof. Stamm’s work includes contributions to domain decomposition methods, polarization energy calculations, and software development like the ddX library. His publications span topics such as model order reduction, quantum simulations, and molecular dynamics. Collaborations and software tools underscore his commitment to bridging computational methods with real-world scientific challenges.

Constantin Grigoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Uncertainty Quantification
• Stochastic Differential Equ://grigo.userweb.mwn.de/ueber-mich/
• +۲ مورد دیگر

Constantin Grigo is a PhD researcher at the Technical University of Munich (TU Munich), actively engaged in the Continuum Mechanics group. His work focuses on Uncertainty Quantification (UQ) and Machine Learning (ML), particularly for applications in maritime safety, bicycle traffic modeling, and stochastic systems. He has presented his research at major conferences like SIAM UQ and WCCM, and has been recognized with Student Travel Awards from SIAM UQ 2018 and SIAM CSE 2019. Education: Master of Science in Physics, LMU Munich (2015) Bachelor of Science in Physics, LMU Munich (2012) Year abroad at Grenoble INP (2010-2011) Research Interests: Probabilistic machine learning for coarse-graining high-dimensional systems Bayesian model and dimension reduction Stochastic differential equations in heterogeneous media Microscopic traffic simulation for bicycles and autonomous vehicles Digital twin applications for maritime and urban mobility Reduced-order modeling of random materials Selected Awards: SIAM UQ 2018: Student Travel Award Winner SIAM CSE 2019: Student Travel Award Winner His publications span topics such as data-driven scenario specification for autonomous vehicles, bicycle maneuver prediction using neural networks, and physics-constrained surrogates for UQ. He also contributes to open-source simulation tools like SUMO for traffic modeling.

Günther Grünمشاهده پروفایل

استاد

• Stochastic Partial Differential Equ (SPDE)
• Free Boundary Problems
• Continuum Mechanics
• +۵ مورد دیگر

Prof. Dr. Günther Grün is a faculty member at the University of Erlangen-Nuremberg , specializing in Applied Mathematics . His work bridges rigorous mathematical analysis, numerical methods, and applications in continuum mechanics , fluid dynamics , and free boundary problems . He serves as Spokesperson for the DFG Research Training Group IntComSin (2022–2027), focusing on interfaces, complex structures, and singular limits in mathematical modeling and simulation. Education Diploma in Mathematics (1991), University of Bonn PhD in Mathematics (1994), University of Bonn Habilitation in Mathematics (2001), University of Bonn Research Interests center on degenerate parabolic equations (e.g., porous-medium and thin-film equations), stochastic SPDEs with multiplicative noise, diffuse interface models for two-phase flow with surfactants, and dimension-reduced models via singular limits. His work combines calculus of variations , homogenization , and Gamma-convergence for multi-scale analysis, alongside finite-element/finite-volume schemes for efficient simulations. Scientific Awards Marie Curie Research Fellowship (1996), Universita di Tor Vergata, Rome Advising Legacy includes mentoring doctoral candidates such as Dr. Fabian Klingbeil (Accenture), Dr. Jürgen Becker (Fraunhofer Institute), and Prof. Dr. Julian Fischer (IST Austria), who have pursued careers in academia, industry, and research institutions.

Sabine C. Langerمشاهده پروفایل

استاد

• Acoustics
• Modeling and Simulation
• Numerical Acoustics
• +۷ مورد دیگر

Prof. Dr.-Ing. Sabine C. Langer is a Full Professor of Acoustics and Director of the Institute of Acoustics at Technische Universität Braunschweig. She holds a PhD in Engineering and has extensive experience in academia, including leadership roles such as President of the Deutsche Gesellschaft für Akustik (DEGA) and Deputy Speaker of the DFG Collaborative Research Center 880 (SFB 880). Her research focuses on acoustics, numerical modeling, aircraft noise reduction, and innovative materials for sound absorption. She has pioneered studies on acoustic black holes, metamaterials, and AI-driven design optimization. Langer’s work also includes contributions to educational platforms, such as developing MATLAB-based sound quality analysis tools and online learning resources for engineering students. Education: Civil Engineering degree (1991–1996, TU Braunschweig), PhD in Engineering (2001, TU Braunschweig). Key positions include W2 Professor for Vibroacoustics (2013–2018) and Junior Professor for Wave Propagation and Building Acoustics (2003–2013). She led the Graduate School at SFB 880 and advised numerous research initiatives in structural acoustics and noise mitigation. Research interests span numerical acoustics, sound quality assessment, and sustainable acoustic design. Her recent work emphasizes AI integration in engineering design, stochastic modeling, and additive manufacturing of acoustic materials. She has published extensively on aircraft cabin noise prediction, vibration isolation, and metamaterial applications. Professional roles include membership in the DIN/VDI Normenausschuss Akustik and the Advisory Board of the Excellence Cluster Hearing4All. She has organized major acoustics conferences, including DAGA 2020 in Hannover, and contributed to standard-setting in noise reduction and vibration technology.

• Numerical Methods
• Computational Engineering
• Multiphysics Simulation Techniques
• +۲ مورد دیگر

Prof. Thomas Weiland is a Full Professor of Computational Electromagnetics at the Technische Universität Darmstadt since 1989. His research focuses on numerical methods, computational engineering, and multiphysics simulation techniques, particularly in accelerator physics and beam dynamics. He holds a Dr.-Ing. from TU Darmstadt and has held postdoctoral and research positions at CERN and TU Darmstadt. His work includes pioneering contributions to electromagnetic field simulations, including advanced finite element methods, discontinuous Galerkin techniques, and boundary element approaches. Education highlights include his Diplom in Electrical Engineering from TU Darmstadt (1975) and a Habilitation in Experimental Physics from the University of Hamburg (1984). His research spans computational electromagnetics, accelerator physics, and numerical methods for electromagnetic field problems. Notable areas of innovation include transparent boundary conditions, eigenmode calculations, and high-performance simulation frameworks for rotating systems and particle accelerators. His publications emphasize advancements in electromagnetic simulation tools, such as the MagPEEC method and Trefftz-discontinuous Galerkin approaches. Collaborative projects include modeling RF photoinjectors for light sources and electrohydrodynamic droplet dynamics. Technical contributions also extend to wake field analysis in particle accelerators and SAR distribution studies in bioelectromagnetics. Research interests further include multiphysics coupling (thermal-electromagnetic effects in surge arresters), stochastic modeling of electromagnetic systems, and field-circuit co-simulation techniques. His work addresses challenges in large-scale eigenvalue problems, adaptive mesh optimization, and high-precision numerical methods for complex geometries.

Jan Mehnerمشاهده پروفایل

استاد

• Microsystems (MEMS)
• MEMS Design
• Modeling and Simulation of Coupled Fields
• +۷ مورد دیگر

Professor Jan Mehner is a faculty member at Chemnitz University of Technology, where he holds the Chair of Microsystems and Medical Technology within the Faculty of Electrical Engineering and Information Technology. His work focuses on advanced modeling, simulation, and design automation of microelectromechanical systems (MEMS) with applications across industrial, automotive, and biomedical domains. His research expertise spans MEMS design , coupled field simulations , finite and boundary element methods (FEM/BEM) , and reduced order modeling (ROM) . These methodologies enable efficient development and optimization of complex microsystems. Applications of his work include structural health monitoring, sensor integration, and smart systems for automation and medical technology. Prof. Mehner is actively contributing to the academic and research community, recently recognized through membership in the Saxon Academy of Sciences. He is involved in cross-disciplinary initiatives such as the Robotic Institute Germany (RIG) at TU Chemnitz, demonstrating strong engagement in emerging technological frontiers. He advises students and leads a research group focused on microsystem innovation. While specific grant details are not listed, his participation in DFG-funded research groups like FOR 5242 suggests active involvement in nationally supported scientific projects. The laboratory under his leadership, the Professorship of Microsystems and Medical Technology, develops cutting-edge solutions in sensor-integrated materials and intelligent microsystems, contributing to advancements in smart manufacturing and medical devices.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

Silke Glasمشاهده پروفایل

پژوهشگر ارشد

• Reduced basis method
• Variational inequalities
• Wave equations
• +۳ مورد دیگر

Silke Glas is a Postdoctoral Researcher at the Institute of Numerical Mathematics, Ulm University, a position she has held since July 2018. Previously, she served as a Research Assistant at the same institute from April 2016 to July 2018 and at the Chair of Energy Trading and Finance, University of Duisburg-Essen (2012-2016), funded by the German Research Foundation's Priority Programme 1324. Her research visits include the Institut Henri-Poincaré in Paris and SISSA in Trieste. Her educational background features: Diploma in Mathematics and Economics from Ulm University (2006-2012), thesis on "Reduced Basis Method for Variational Inequalities" Master of Mathematics from the University of South Florida (2009-2010) Dr. Glas specializes in model reduction for nonlinear problems, with core expertise in reduced basis methods applied to variational inequalities, wave equations, Hamilton-Jacobi-Bellman equations, and space-time formulations. Her work bridges theoretical numerical analysis with practical applications in energy markets, particularly intraday electricity trading. She has developed novel approaches for noncoercive and parabolic systems, addressing challenges in error estimation and computational efficiency. Her publication trajectory reveals evolving sophistication in handling time-dependent nonlinear systems, with increasing emphasis on financial applications. Early work focused on theoretical foundations of variational inequalities, while recent publications integrate model reduction with optimal control for energy trading problems, demonstrating cross-disciplinary impact. Scientific recognition includes: No formal awards documented in source material Research funding has been secured through the German Research Foundation's Priority Programme 1324. No student advising activities are mentioned, though her collaborative work involves prominent researchers like K. Urban and Anthony T. Patera. Her primary research environment at Ulm University's Institute of Numerical Mathematics supports her focus on computational mathematics and real-world applications.