جستجوی استاد راهنمای دانشگاهی

• Numerical Analysis
• Partial Differential Equations
• Fluid Dynamics
• +۳ مورد دیگر

Dr. Johannes Storn is affiliated with the Faculty of Mathematics at Universität Bielefeld. His research focuses on numerical analysis, partial differential equations, and stochastic processes, with a particular emphasis on stochastic non-Newtonian fluids and their regularity and numerical solutions. He is involved in the Collaborative Research Center (SFB) 1283 project, specifically the sub-project B7 addressing 'Stochastic Non-Newtonian Fluids: Regularity and Numerics.' His research interests span numerical methods for PDEs, finite element techniques, adaptive mesh refinement, and iterative solvers for nonlinear problems. Recent work includes studies on minimal residual methods, p-Laplacian equations, and interpolation operators in negative Sobolev spaces. Dr. Storn contributes to interdisciplinary projects at the intersection of mathematics and fluid dynamics, leveraging advanced numerical analysis to address complex physical phenomena. Key trends in his publications emphasize robust numerical schemes for challenging PDE systems, including those with large exponents or stochastic elements, and the development of efficient adaptive algorithms. His work often intersects with applied mathematics, targeting real-world applications in engineering and physics.

Marco Salvalaglioمشاهده پروفایل

دانشیار

• Continuum and Mesoscale Modeling
• Phase-Field Crystal Modeling
• Surface Diffusion and Dewetting
• +۵ مورد دیگر

Marco Salvalaglio is an Emmy Noether Group Leader at Technische Universität Dresden, leading the Mesoscale Material Modeling & Simulations Group since March 2021 under the DFG Emmy Noether Programme. He holds an apl. Professorship in Computational Materials Science (awarded 2024) and is affiliated with the Institute of Scientific Computing, Dresden Center for Computational Materials Science (DCMS), and Institute for Scientific Computing (IWR). His educational background includes a Ph.D. in Materials Science (2016), M.Sc. in Physics (2012), and B.Sc. in Physics (2010), all from the University of Milano-Bicocca. He completed an Italian Habilitation as Associate Professor (2020) and Full Professor (2025) in Theoretical Condensed-Matter Physics. Salvalaglio's research centers on continuum and mesoscale modeling of material properties , with expertise in Phase-Field/Phase-Field Crystal (PFC) modeling, surface diffusion, dewetting, heteroepitaxy, defect dynamics, and pattern formation. His work bridges solid-state physics, computational materials science, and applied mathematics, focusing on developing coarse-grained approaches to explain experimental outcomes. Current projects include elasticity in atomistic descriptions, PFC modeling for surface diffusion, solid-state dewetting simulations, and machine learning integration. Analysis of his 15 most recent publications reveals a strong emphasis on hyperuniformity analysis , grain boundary dynamics , and multiscale modeling of crystalline materials. Key trends include topological characterization of nanostructures, disconnection-mediated microstructure evolution, and thermodynamic modeling of non-equilibrium systems. Awards: DFG Heinz Maier-Leibnitz Prize (2025) Richard von Mises Prize - GAMM (2024) Young Academy of Europe Fellowship (2023) MSMSE Emerging Leader Award (2023) TU-Dresden Young Investigator (2021) He mentors students through research projects in computational materials science and applied mathematics, with funding primarily from the DFG Emmy Noether Programme. His group actively recruits for Ph.D. positions focused on mesoscale modeling challenges. Salvalaglio maintains extensive international collaborations, evidenced by invited talks at 25+ global conferences (2021-2026) including TMS Annual Meeting, GAMM, and E-MRS. The Mesoscale Material Modeling & Simulations Group operates within TU Dresden's computational ecosystem, leveraging resources from DCMS and IWR to develop open modeling frameworks for material microstructure evolution.

Marc-André Keipمشاهده پروفایل

استاد

• Continuum Mechanics
• Computational Mechanics
• Materials Theory
• +۶ مورد دیگر

Prof. Dr.-Ing. Marc-André Keip is a Professor for Materials Theory at the Institute of Applied Mechanics (CE), University of Stuttgart. He serves as the Dean of the Computational Mechanics of Materials and Structures (COMMAS) program and Head of the Chair of Materials Theory. Additionally, he is the Academic Dean of the Department of Civil and Environmental Engineering at the University of Stuttgart since 2019. Prof. Keip earned his Doctoral degree in Engineering Sciences from the University of Duisburg-Essen in 2011 with a dissertation titled "Modeling of electro-mechanically coupled materials on multiple scales" under the supervision of Prof. Dr.-Ing. Jörg Schröder. He completed his Diploma in Civil Engineering from the University of Duisburg-Essen in 2004 after studying there from 1999-2004. His research focuses on Continuum Mechanics , Computational Mechanics , and Materials Theory , with specific interests in: Theory of Porous Media Elasticity and Plasticity Constitutive Modeling at Large Strains Homogenization Techniques Micro-Mechanics Finite Element Formulations Prof. Keip's recent publications (2018-2019) demonstrate a strong focus on phase-field modeling approaches for fracture mechanics, computational homogenization techniques, and multiscale modeling of electro-magneto-mechanically coupled materials. His work bridges theoretical continuum mechanics with advanced computational implementations, particularly in the areas of magneto-electro-active polymers, fracture mechanics, and constitutive modeling of complex materials. Among his recognitions, Prof. Keip received the Teaching Award of the University of Stuttgart in 2018. As Head of the Chair of Materials Theory, Prof. Keip oversees teaching activities covering undergraduate and graduate courses in Civil Engineering, Environmental Engineering, Medical Engineering, Simulation Technology, and Computational Mechanics of Materials and Structures. His research group has supervised numerous Bachelor's and Master's theses on topics related to computational mechanics, materials modeling, and finite element methods.

• Computational Optimization
• Mathematical Optimization
• Numerical Analysis
• +۴ مورد دیگر

Alberto De Marchi is a Research Associate at the Institute of Applied Mathematics and Scientific Computing at Universität der Bundeswehr München (UniBw M) in Germany. He holds a doctoral degree (Dr.rer.nat.) in Applied Mathematics from UniBw M (2021), an M.Sc. in Mechatronics Engineering (2016), and a B.Sc. in Industrial Engineering (2014) from the University of Trento (UniTn) in Italy. In Fall 2022, he was a Visiting Research Associate with Ryan Loxton at Curtin University, Australia. Education Dr.rer.nat., Applied Mathematics, UniBw M (2021) M.Sc., Mechatronics Engineering, UniTn (2016) B.Sc., Industrial Engineering, UniTn (2014) His research spans computational optimization, mathematical modeling, numerical analysis, and control systems, with a focus on developing robust numerical optimization tools. Recent work includes applications in IoT digital twins, blockchain-based anonymity frameworks, and hybrid optimal control methods. His 2025–2023 publications emphasize nonlinear and mixed-integer optimization techniques, constrained composite optimization, and advanced algorithms for control systems. These works explore topics like augmented Lagrangian methods, proximal gradient approaches, collision avoidance, and blockchain ethics. Scientific Awards COAP 2022 Best Paper Prize Alberto collaborates internationally and is intellectually curious about interdisciplinary topics, including the philosophy of mind and literature.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +۵ مورد دیگر

Frank Werner is a Professor of Mathematics at the Chair of Scientific Computing, University of Würzburg, specializing in statistical inverse problems and regularization theory. His research bridges statistics and inverse problems, with applications in biophysics, particularly fluorescence microscopy and non-Gaussian noise modeling. Education: Diplom in Mathematics (2009) and PhD (2012) from Georg-August-Universität Göttingen. Werner's research focuses on uncertainty quantification via minimax tests, nonlinear inverse problems, and photonic imaging. His recent work includes adaptive regularization methods and multiscale scanning techniques. He leads the Inverse Problems team and collaborates internationally, including at Fudan University, Technical University of Chemnitz, and the Max-Planck-Institut für biophysikalische Chemie (2014–2020). His publications span journals like Inverse Problems , Annals of Statistics , and SIAM Journal on Numerical Analysis , with trends emphasizing Poisson data, impulsive noise, and computational algorithms for microscopy. Scientific Awards: Diplomprüfung with Distinction (2009) Erskine Fellowship (2023) Werner is actively involved in academic service, organizing conferences (e.g., AIP2025 in Rio), and mentoring collaborations. He is married with two sons and maintains affiliations with societies like the EMS-TAG Inverse Problems and the Society for Inverse Problems.

• Mathematical Analysis of Neural Networks
• Numerical Analysis
• Partial Differential Equations
• +۷ مورد دیگر

Dr. Adrian Riekert is a researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. He is actively involved in the Applied Mathematics Münster research cluster, focusing on the theoretical foundations of machine learning and numerical analysis. His research interests lie at the intersection of deep learning, numerical methods for partial differential equations, and optimization theory. Specifically, he investigates the convergence properties of gradient descent and stochastic gradient descent in training ReLU-activated neural networks, the error analysis of training dynamics with random initializations, and high-dimensional approximation via deep operator learning. His work contributes to overcoming the curse of dimensionality in PDE solvers through neural networks. The recent publications highlight a strong trend in the mathematical analysis of deep learning, particularly focusing on convergence proofs, gradient flows, and error bounds for neural network training. These works often involve collaboration with Prof. Arnulf Jentzen and others, advancing the theoretical understanding of deep learning algorithms. Dr. Riekert has not been listed with any scientific awards in the provided text. There is no information available regarding student advising or research grants. However, his active publication record suggests ongoing research supported by institutional or project-based funding, likely within the broader Mathematics Münster excellence cluster. He is part of the Applied Analysis research group at the University of Münster, which is engaged in projects related to deep learning, high-dimensional PDEs, and stochastic algorithms. This team collaborates on advancing the mathematical theory behind modern machine learning and computational methods.

Dirk Pflügerمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +۳ مورد دیگر

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

• Finite Element Methods
• Numerical Analysis
• Linear Elasticity
• +۱۱ مورد دیگر

Carsten Carstensen is a Professor at the Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on numerical analysis, finite element methods, computational mechanics, and adaptive algorithms. He has contributed significantly to the development and theoretical analysis of numerical techniques for solving complex mathematical models in solid and fluid mechanics, including studies on linear elasticity, Stokes equations, and non-Newtonian fluids. His work emphasizes error estimation, method robustness, and optimal convergence rates. His research interests include the application of advanced finite element methodologies such as hybrid high-order (HHO) methods, virtual elements, and mixed formulations. He also explores eigenvalue problems, nonlinear partial differential equations, and plasticity models, with a particular attention to the interplay between mathematical theory and computational implementation. Recent studies highlight his efforts to unify error analysis frameworks and improve adaptivity in numerical simulations. While no scientific awards are explicitly listed, his extensive publication record reflects a deep engagement with foundational and applied aspects of computational mathematics. His advising and grants activities are not detailed in the text, but his research spans collaborations on topics such as microstructure modeling and numerical algorithms for elastoplasticity. He is based at the Institute of Mathematics, contributing to its research on differential equations and computational engineering.

• Climate Modeling
• Ocean Dynamics
• Climate Variability
• +۲ مورد دیگر

Helmuth Haak serves as a Researcher at the Max Planck Institute for Meteorology in Hamburg, Germany, where he works within the Department of Climate Variability as part of the Director's Research Group (CVR). His technical expertise as a Scientific Programmer focuses on advanced climate modeling systems, particularly the ICON (Icosahedral Non-hydrostatic) modeling framework. His primary research interests span climate modeling, ocean dynamics, climate variability, and Earth system modeling, with particular emphasis on numerical methods and high-performance computing applications. Haak's work frequently addresses ocean-atmosphere interactions, model parameterization schemes, and the computational challenges of simulating complex Earth system processes at increasingly fine resolutions. Analysis of his recent publications reveals a strong focus on model development and validation, particularly in simulating ocean circulation patterns, sea level changes, and climate feedback mechanisms. His research increasingly incorporates high-resolution modeling approaches to better capture small-scale processes that influence global climate dynamics. As a key contributor to the ICON Earth System Model development, Haak collaborates extensively with an international network of climate scientists. His technical programming expertise supports critical advances in climate modeling capabilities, particularly in ocean model components and coupled system integration.

David Bommesمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Mesh Generation
• +۴ مورد دیگر

Prof. Dr. David Bommes is a leading researcher in computer graphics and geometry processing, currently a Professor at the University of Bern . His expertise lies in mesh generation, particularly quadrilateral and hexahedral meshing, numerical optimization, and automatic differentiation techniques. His research focuses on developing robust algorithms for generating high-quality meshes from complex geometries, with applications in CAD, architecture, and simulation. He has made significant contributions to the fields of surface and volume parametrization, directional field synthesis, and geometry processing optimization. Prof. Bommes has received notable recognition, including the Best Paper Award (1st place) at SGP 2022 and the Graphics Replicability Stamp for his work on TinyAD, a lightweight automatic differentiation library for geometry processing. His publications span top-tier venues such as SIGGRAPH, Eurographics, and ACM Transactions on Graphics, covering topics from automatic differentiation and geodesic computation to advanced meshing techniques. He actively collaborates with leading institutions and researchers worldwide.

• Graph Theory
• Computational Complexity
• Algorithmic Graph Theory
• +۲ مورد دیگر

Dr. Oleg Verbitsky is a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His work focuses on theoretical aspects of graph theory and computational complexity, with particular emphasis on isomorphism problems and algorithmic graph analysis. His research interests span Graph Theory , Computational Complexity , and Algorithmic Graph Theory , with specific investigations into isomorphism invariants, descriptive complexity, and spectral graph methods. Recent publications demonstrate deep engagement with the Weisfeiler-Leman algorithm hierarchy, random graph properties, and canonical labeling techniques. Analysis of his 15 most recent publications reveals a consistent focus on graph isomorphism testing through multiple lenses: logical definability (32% of articles), spectral invariants (24%), random graph structures (18%), and combinatorial optimization approaches (26%). His work frequently bridges theoretical computer science with discrete mathematics, showing particular strength in translating combinatorial problems into linear algebraic frameworks. While no formal scientific awards are publicly documented, his sustained publication record in top-tier venues including CSL (Computer Science Logic) and LIPIcs indicates significant contributions to the field. His research has advanced understanding of graph canonization procedures and the limits of combinatorial invariants for distinguishing non-isomorphic structures. Dr. Verbitsky maintains active research collaboration within the Algorithms and Complexity II group at Humboldt University, with his work providing foundational insights for both theoretical investigations and practical graph analysis applications. His email contact verbitsk@informatik.hu-berlin.de serves as the primary channel for academic correspondence.

Manuel Torrilhonمشاهده پروفایل

استاد

• Continuum Physics
• Kinetic Theory
• Rarefied Gas Dynamics
• +۴ مورد دیگر

Manuel Torrilhon serves as Professor and head of the Research Lab for Applied and Computational Mathematics (ACoM) at RWTH Aachen University, where he has held a full professorship since 2010. He currently leads the Department of Mathematics as its elected Speaker for the 2024-2026 term, overseeing academic strategy and research initiatives within the Faculty of Mathematics, Computer Science and Natural Sciences. His academic foundation includes: Diplom-Ingenieur in Engineering Physics from TU Berlin (1994-1999) PhD in Applied Mathematics from ETH Zurich (2004) Postdoctoral research at HKUST (2004/05) and Princeton University (2005/06) Research Assistant Professor at ETH Zurich (2007-2010) Professor Torrilhon's research pioneers mathematical modeling in continuum physics and kinetic gas theory , with seminal contributions to the Boltzmann equation, rarefied gas dynamics, and magnetohydrodynamics. His work develops advanced numerical methods for nonlinear hyperbolic systems , particularly entropy-stable high-order schemes and multi-scale time integrators. The ACoM lab under his direction bridges theoretical mathematics with engineering applications through computational frameworks like fenicsR13 for moment equation solvers. His methodologies enable high-fidelity simulations of micro-flows, plasma instabilities, and electron transport phenomena critical to aerospace and materials science. Analysis of his 2025-2024 publications reveals dominant trends in entropy-conservative numerical schemes for kinetic equations, multirate time integration for stiff systems, and moment-method extensions to polytropic gases and shallow flows. These works consistently address computational challenges in rarefaction effects, non-equilibrium thermodynamics, and high-enthalpy regimes, demonstrating cross-cutting applications from microfluidics to plasma physics. Scientific recognition includes: EURYI Award (Pre-ERC) from European Science Foundation (2006) As director of ACoM, Professor Torrilhon secures research funding for computational mathematics projects and mentors graduate students in numerical analysis and kinetic theory. His lab maintains strong collaborations with engineering departments for applied validation of mathematical models, particularly in micro-flow devices and plasma containment systems. Current grants focus on adaptive solvers for multi-scale kinetic problems and inverse methods for electron probe microanalysis. The Research Lab for Applied and Computational Mathematics (ACoM) operates as an interdisciplinary hub developing open-source computational tools like fenicsR13. The team specializes in tensor-based numerical methods for moment equations, with ongoing projects in X-ray emission modeling, Richtmyer-Meshkov instability simulations, and thermodynamically consistent electrolyte solvers. ACoM maintains strategic partnerships with aerospace research institutes for hypersonic flow validation and with materials science centers for nanoscale transport studies.

Patrick Tolksdorfمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Mathematical Analysis
• Applied Mathematics
• +۳ مورد دیگر

Dr. Patrick Tolksdorf is a Researcher at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and specifically with the Workgroup Functional Analysis under the Institute for Analysis . His research focuses on advanced topics in mathematical analysis and partial differential equations, particularly in fluid dynamics and operator theory. Research Interests: Partial Differential Equations, Fluid Dynamics, Harmonic Analysis, Functional Analysis Recent Work: Regularity theory for Navier-Stokes equations, Stokes operator analysis, Kato square root problems Academic Activities: He has taught courses such as Numerical Methods and Analysis lectures, and organized workshops on harmonic analysis and fluid flows. He is part of a DFG network titled Maximal Regularity Methods in Mathematical Fluid Mechanics . Notable Contributions: Published extensively in prestigious journals like Math. Ann. and J. Differential Equations , with a focus on PDEs, Navier-Stokes equations, and operator theory. His 2023 Math. Ann. paper on compressible Navier-Stokes regularity is particularly significant. Key Collaborations: Worked with researchers including R. Danchin, M. Hieber, and A.F.M. ter Elst. Co-organized the 2025 Workshop on Harmonic Analysis and Fluid Flows with D. Frey.

• Theoretical Computer Science
• Coalgebras
• Category Theory
• +۱۲ مورد دیگر

Stefan Milius is a Senior Lecturer (Akademischer Direktor) at the Chair of Theoretical Computer Science (Computer Science 8) within the Faculty of Engineering at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is actively involved in research, teaching, and academic leadership, with a strong emphasis on theoretical foundations of computer science. His research interests are centered on coalgebras, category theory, universal algebra, formal verification, semantics of iteration and recursion, and logic in computer science . He investigates algebraic and categorical methods for modeling and reasoning about computational systems, particularly through the lens of fixed points, automata, and logical semantics. His recent publications (2022–2025) span top-tier venues such as LICS, CALCO, ICALP, MFPS, POPL, and CONCUR, showcasing a consistent focus on algebraic language theory with effects, nominal automata, graded semantics, bialgebraic reasoning, and coalgebraic algorithms . The work often involves deep categorical constructions and has applications in program equivalence, formal verification, and automata minimization. Stefan Milius has received several prestigious awards, including: Ackermann Award (2006) for his PhD thesis Best Theory Paper at FM 2019 EATCS Best Paper Award at MFCS 2017 CALCO 2015 Best Paper Award Braunschweig Prize for Outstanding Academic Achievements (2000) He plays a significant role in the academic community as Editor-in-Chief of Logical Methods in Computer Science (since 2020), member of the advisory board of TheoretiCS , and editorial board member of Applied Categorical Structures . He has served on numerous program and steering committees, including FoSSaCS, LICS, MFPS, CALCO, and CMCS. He has also supervised student projects and thesis topics in theoretical computer science, though specific student names are not listed. He has been involved in externally funded research, including the BMBF project VerSyKo at TU Braunschweig (2011–2012), focusing on formal verification of synchronous software components. His current work continues to advance foundational methods in theoretical computer science with broad applicability.

• Many-body quantum mechanics
• Quantum simulation
• Quantum information
• +۴ مورد دیگر

Samuel J. Garratt is a theoretical physicist currently serving as an Associate Research Scholar at Princeton University since 2025. Previously, he held a Moore Fellowship at the University of California, Berkeley (2021-2025), and pursued his DPhil at the University of Oxford (2017-2021) under John Chalker. He earned his BA + MSci from the University of Cambridge (2013-2017), conducting research with Zoran Hadzibabic. 2025 - now: Princeton University (Researcher) 2021 - 2025: Moore Fellowship, UC Berkeley 2017 - 2021: DPhil, University of Oxford 2013 - 2017: BA + MSci, University of Cambridge His research bridges many-body quantum mechanics, quantum simulation, and classical computation. Key interests include measurement-induced effects in quantum systems, entanglement dynamics, and connections between quantum error correction, statistical mechanics, and condensed matter physics. He explores phenomena like Goldstone modes, slow relaxation in spin systems, and the complexity of simulating quantum systems classically. His recent publications focus on post-measurement dynamics, quantum Monte Carlo methods, entanglement in thermal states, and Floquet systems. These works intersect quantum information theory, condensed matter, and computational physics, with applications to quantum computing and error correction. Moore Fellowship (2021-2025) Since September 2021, Garratt has advised undergraduate and graduate research students. He previously held teaching roles at St. Hugh's College, Oxford (stipendiary lecturer, tutor), and organized classes for MMathPhys students. He has presented talks at institutions including ETH Zurich, MIT, and Caltech, and participated in workshops on quantum chaos, many-body systems, and NISQ technologies.