جستجوی استاد راهنمای دانشگاهی

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 .

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.

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.

Felix Fritzenمشاهده پروفایل

استاد

• Data Analytics in Engineering
• Computational Mechanics
• Material Modeling
• +۷ مورد دیگر

Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).

• Applied Mathematics
• Computational Methods
• Control Theory
• +۵ مورد دیگر

Sara Grundel is a leading researcher at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her work focuses on computational methods in systems and control theory, particularly in model order reduction, gas network simulation, and optimization of energy systems. Education: Diplom in Mathematics, ETH Zurich (2005) PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University (2011) Research Interests: Sara’s research encompasses mathematical control theory, stability analysis, and numerical methods for differential-algebraic equations. She applies these techniques to gas and energy networks, epidemic modeling, and multi-agent systems. Her interdisciplinary work bridges computational mathematics with real-world engineering and public health challenges. Recent Publications: Her 15 most recent articles (2024–2012) demonstrate expertise in parametrized PDEs, model reduction for coupled systems, and control strategies for SARS-CoV-2 containment. Key subtopics include adaptive meshing, stability-preserving algorithms, and optimization of nonlinear network dynamics. Scientific Contributions: Developed clustering-based model reduction techniques for networked systems Investigated hyperbolic discretization methods using Riemann invariants Advanced polynomial root radius optimization with affine constraints Collaborations: Sara frequently collaborates with researchers like Peter Benner and Martin Gersen on energy grid simulations and control theory. She participates in international conferences (GAMM, IEEE CDC, MTNS) and contributes to edited volumes in applied mathematics.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

Felix Schindlerمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

Felix Schindler is a Researcher at the Institute for Analysis and Numerical Analysis , part of the Department of Mathematics and Computer Science at the University of Münster . His work bridges numerical analysis, machine learning, and scientific computing, with a focus on model reduction for partial differential equations (PDEs), adaptive algorithms, and computational efficiency. Research Interests include: Numerical analysis of parametric and multiscale PDEs Localized reduced basis methods (LRBM) and adaptive enrichment Integration of model order reduction (MOR) with machine learning (ML) Conservative flux reconstruction techniques Development of software libraries like dune-xt and pyMOR Recent Publications highlight trends in applying deep kernel models for surrogate modeling, localized training strategies for PDE-constrained optimization, and hybrid full/reduced-order pipelines for reactive flow prediction. His work emphasizes certified error control, hierarchical adaptivity, and cross-disciplinary computational frameworks. Collaborations span institutions such as AIMS Senegal, Springer Nature, and DUNE project teams. He actively contributes to conferences like GAMM, ENUMATH, and Algoritmy.

Ullika Scholzمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Fluid Dynamics
• Computational Engineering
• +۵ مورد دیگر

Ullika Scholz is a Ph.D. candidate at the Institute of Applied Mathematics (ACoM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. Ph.D. candidate (03/2020–present) M.Sc. in Mathematics (Free University of Berlin, 2019) B.Sc. in Mathematics (Free University of Berlin, 2014) Her research focuses on dimensionally reduced equation systems for incompressible free surface flows, extending moment approximations and model cascades for shallow flow. She works with PDE systems derived via vertical polynomial expansions, incorporating dispersive effects beyond hydrostatic pressure assumptions. Her scientific work intersects applied mathematics, computational fluid dynamics, and numerical analysis. She has participated in international conferences such as ICCE 2024 and MMKT IV, presenting research on shallow moment equations and spline basis functions for kinetic theory models. Rosa-Luxemburg-Foundation Scholarship recipient (2013–2019)

Gianluigi Rozzaمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Fluid Dynamics
• Scientific Computing
• +۶ مورد دیگر

Gianluigi Rozza is a Full Professor in Numerical Analysis and Scientific Computing at SISSA (International School for Advanced Studies) in Trieste, Italy. He leads the SISSA mathLab research group and serves as Coordinator of the Mathematics Area at SISSA. He holds roles such as SISSA Director's Delegate for Innovation and Knowledge Transfer and member of various committees including the SISSA HPC Committee and SIAM Student Chapter. His research focuses on numerical analysis, computational fluid dynamics, reduced order methods (ROM), and their applications in engineering and biomedical fields. Education: Laurea cum Laude in Aerospace Engineering from Politecnico di Milano (2002), PhD in Applied Mathematics from EPFL (2005). Postdoctoral research at MIT (2006-2008), followed by roles at EPFL and SISSA. He has supervised over 30 PhD and Master’s students, with expertise in reduced order modeling and computational mechanics. Research Interests: Reduced basis methods, parametrized PDEs, fluid-structure interaction, cardiovascular simulations, environmental flows, machine learning integration, and industrial collaborations with companies like Danieli and Fincantieri. He coordinates EU projects such as the ERC Consolidator Grant AROMA-CFD (2016-2021) and the ITN ROMSOC project. Publications: Over 90 scientific papers, two books as editor, and contributions to open-source software like rbMIT and ITHACA-FV. Awards include the ECCOMAS Young Investigator Award (2014), SIAM Fellowship (2025), and the Gili Agonistelli Prize (2025). Grants and Projects: Principal Investigator of ERC AROMA-CFD and ARGOS PoC. Coordinates regional projects like UBE (Under Water Blue Efficiency) and TRIM. Engages in environmental modeling (e.g., SOPHYA, PRELICA) and industrial collaborations under MAR tc FVG. Labs/Teams: SISSA mathLab develops open-source tools for computational science, including ROM libraries and fluid dynamics solvers. Active in organizing workshops, serving on editorial boards (e.g., SIAM Journals), and advisory roles for agencies like ARPA FVG.

Constantin Greifمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Partial Differential Equations (PDEs)
• Approximation Theory
• +۴ مورد دیگر

Constantin Greif is a Ph.D. student and scientific employee at the Institute for Numerical Mathematics , Ulm University , Germany. He holds a Master of Science in Mathematics from both the University of Wisconsin-Milwaukee (2017) and Ulm University (2018) , as well as a Bachelor of Science in Mathematics and Management (2015). Research Interests: Greif focuses on N-widths of parametric PDEs , approximation methods using ridge functions , reduced basis methods , and numerical methods for Hamilton-Jacobi-Bellman equations . His work lies at the intersection of applied mathematics , computational science , and model order reduction . Academic Contributions: He has authored peer-reviewed articles on Kolmogorov N-width decay and ridge-based linear approximations. His conference activities include talks at the Young Mathematicians in Model Order Reduction (YMMOR) and SIAM Conference on Computational Science and Engineering . Contact: Email: constantin.greif@uni-ulm.de Office: Helmholtzstr. 20, Room 1.28, Ulm University Office Hours: By appointment via email.

• Thermal Hydraulics
• Nuclear Engineering
• Reactor Safety
• +۳ مورد دیگر

Christoph Schuster is a Faculty member at the Technische Universität Dresden under the Institute of Process Engineering and Environmental Technology , specifically in the Chair of Hydrogen and Nuclear Energy . He serves as Head of the Thermohydraulics Research Area and has led the Thermohydraulics Working Group from 2008 to 2020. His academic role as a Researcher focuses on Thermal Hydraulics and Nuclear Reactor Safety . PhD in Nuclear Energy Technology (1992) Studies in Energy System Technology (1982-1987) Research Interests include: Thermal Hydraulics of nuclear reactors Passive safety system effectiveness Spent fuel pool safety analysis Two-phase flow in natural circulation loops Reactor accident scenario modeling Article Trends show a consistent focus on Thermal Hydraulics , Nuclear Safety , and Two-Phase Flow , with recent work emphasizing Spent Fuel Pool Dynamics and Passive Safety Systems . Teaching Activities include courses on: Basics of nuclear energy technology Thermohydraulics of nuclear reactors Nuclear safety methods Transient power output in nuclear plants

Daniel Peterseimمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Analysis
• Multiscale Methods
• +۶ مورد دیگر

Prof. Dr. Daniel Peterseim is the Chair of Computational Mathematics at the University of Augsburg, with a focus on numerical methods for partial differential equations and multiscale problems. He leads a research team including collaborators like Robert Altmann, Moritz Hauck, and Hannah Mohr. 2017–present: Chair of Computational Mathematics, University of Augsburg 2013–2017: Professor for Numerical Simulation, University of Bonn 2009–2013: Head of Junior Research Group, DFG Research Center Matheon & Humboldt University Berlin His research centers on computational multiscale methods, eigenvalue problems, and wave phenomena, with applications in mechanics, physics, and medicine. Recent work includes numerical stochastic homogenization and quantum computing applications of finite element methods. He has received the ERC Consolidator Grant for his research. Scientific awards: ERC Consolidator Grant He has advised numerous PhD and Master’s students, including Moritz Hauck, Fabian Kröpfl, and Barbara Verfürth. His team collaborates on projects like metamaterial simulations and microstructure reconstruction using neural networks.

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

Dr. Hendrik Kleikamp is a Researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. His research focuses on numerical analysis, machine learning, and scientific computing, with a particular emphasis on nonlinear model order reduction, optimal control of dynamical systems, and scientific machine learning techniques such as neural networks and kernel methods. He is actively involved in international conferences and workshops, including SIAM CSE and MATHMOD, where he has presented talks on topics like adaptive model hierarchies and certified machine learning approaches for parameterized problems. Kleikamp collaborates with institutions globally and contributes to open-source tools like pyMOR. His work bridges theoretical advancements and practical applications in computational science. His research interests include reducing computational complexity in transport-dominated problems, developing efficient algorithms for optimal control scenarios, and integrating machine learning into model reduction frameworks. Recent contributions involve certified algorithms for parametrized systems and knowledge graph development for applied mathematics models. He maintains an active publication record in journals such as ESAIM: Mathematical Modelling and Numerical Analysis and SIAM Journal on Scientific Computing. Kleikamp’s affiliations include Mathematics Münster and the ON-DEM COST Action, where he has conducted workshops on model order reduction. His technical contributions span software development, conference organization, and collaborative research on interdisciplinary computational challenges. Contact: hendrik.kleikamp@uni-muenster.de, Room 120.007.