جستجوی استاد راهنمای دانشگاهی

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +۵ مورد دیگر

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

• Numerical Analysis
• Geometric Numerical Integration
• Structure-Preserving Algorithms
• +۳ مورد دیگر

Elena Celledoni is a Professor of Mathematics at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), where she has been employed since 2004. She leads the research group on differential equations and numerical analysis. Her academic background includes a Master’s degree (1993) and Ph.D. (1997) in mathematics from the Universities of Trieste and Padua, Italy, respectively. She has held postdoctoral positions at the University of Cambridge (UK), the Mathematical Sciences Research Institute (MSRI, Berkeley, CA), and NTNU. Her research focuses on numerical analysis, particularly structure-preserving algorithms for differential equations and geometric numerical integration. Recent work includes applications of neural networks in computational mechanics and data-driven modeling. She has co-authored over 100 peer-reviewed articles in journals such as Journal of Computational Physics , SIAM Journal on Scientific Computing , and Physica D . Her research interests span computational methods for dynamical systems, machine learning integration with numerical analysis, and geometric algorithms for shape analysis. She actively collaborates with international researchers, including contributions to conferences like NeurIPS and workshops on theoretical aspects of computational dynamics. Elena is a member of the editorial boards of Journal of Computational Dynamics and has organized workshops on structure-preserving integrators. Her work emphasizes preserving geometric properties in numerical methods, with applications in fluid dynamics, mechanical systems, and image processing.

Hennes Alexander Hajdukمشاهده پروفایل

پژوهشگر ارشد

• Physical Oceanography
• Influence of Bottom Topography on Oceanic Flows
• Property-Preserving Numerical Methods
• +۳ مورد دیگر

Hennes Alexander Hajduk is a Research Fellow at the University of Oslo's Section for Meteorology and Oceanography, part of the Department of Geosciences. He holds a PhD in Applied Mathematics from TU Dortmund University (2022). His work focuses on physical oceanography, numerical methods for fluid dynamics, and the influence of bottom topography on ocean flows. He develops property-preserving numerical schemes for conservation laws and shallow-water equations, with applications in geophysics and computational fluid dynamics. Education: PhD in Applied Mathematics (TU Dortmund University, 2022). Research Interests: Physical Oceanography: Investigating jet formation in stratified fluids and topographic effects on oceanic flows. Numerical Methods: Specializing in algebraic flux correction schemes, discontinuous Galerkin methods, and entropy-stable algorithms. Geophysical Modeling: Developing tools like FESTUNG for DG-based simulations in MATLAB/Octave. His publications emphasize stability, accuracy, and computational efficiency in simulating complex fluid systems. He collaborates on projects such as The Rough Ocean Research Group, advancing understanding of fluid dynamics in geophysical contexts. Affiliations: Section for Meteorology and Oceanography, University of Oslo Rough Ocean Research Group

• Numerical Analysis
• Stochastic Differential Equations
• Runge-Kutta Methods
• +۴ مورد دیگر

Anne Kværnø is a Professor in the Department of Mathematical Sciences at NTNU. Her research focuses on numerical analysis, particularly stochastic differential equations, Runge-Kutta methods, and geometric integration. She has made significant contributions to the development of high-order numerical schemes for stochastic systems, including exponential integrators and Lawson schemes. Her work emphasizes preserving invariants and stability properties in stochastic and deterministic systems. Key research areas include multirate methods for coupled systems, differential-algebraic equations, and applications in energy storage modeling. She has collaborated extensively on projects involving aquifer thermal energy storage (ATES) systems and groundwater flow analysis. Her publications span prestigious journals like BIT Numerical Mathematics, SIAM Journal on Numerical Analysis, and Journal of Computational Physics. She has presented at major conferences such as ICIAM, SciCADE, and ENUMATH, focusing on stochastic numerical methods and their applications. Her research bridges theoretical developments with practical computational challenges in engineering and environmental modeling.

• Quantum Physics
• High-Energy Physics
• Theoretical Physics
• +۴ مورد دیگر

Alexander Karl Rothkopf is affiliated with the University of Stavanger (UiS), where he holds a position in the Department of Mathematics and Physics under the Faculty of Science and Technology. His research focuses on quantum systems, particularly real-time dynamics of strongly coupled systems like the quark-gluon plasma. He employs lattice QCD, Bayesian inference, and machine learning techniques for non-perturbative studies. Key projects include developing discretization schemes, machine-learning enhanced spectral extraction, and real-time simulation strategies. Rothkopf teaches courses on mechanics, quantum mechanics, and lattice-based simulations. His work bridges theoretical physics with computational methods, addressing challenges in heavy quark dynamics and open-quantum systems. Recent research highlights include exact symmetry conservation in numerical methods, novel discretization techniques for quantum systems, and machine learning applications for complex Langevin simulations. He collaborates internationally with institutions like Brookhaven National Laboratory (BNL) and Linköping University. His contributions span journals such as Journal of Computational Physics and Physical Review D , emphasizing computational physics and high-energy theory. Presentations include talks at the International Symposium on Lattice Field Theory and Nordic Lattice Meetings. Despite no explicit awards listed, his prolific publication record underscores his impactful research in theoretical and computational physics. Education: Advanced training in theoretical physics, likely including a PhD in a related field (details not explicitly provided). Grants/Advising: Leads projects funded by UiS collaborations; advises students on lattice simulations and real-time dynamics (specific grant details not listed). Labs/Teams: Involved in interdisciplinary teams focusing on lattice QCD, computational methods, and open-quantum systems at UiS and partner institutions.

• Numerical Analysis
• Differential Equations
• Geometric Integration
• +۳ مورد دیگر

Brynjulf Owren is a Professor at the Department of Mathematical Sciences, Norwegian University of Science and Technology (NTNU), since January 1994. He holds a Doktoringeniør (PhD) in Numerical Analysis from NTH (now NTNU) and has extensive research experience in numerical analysis, differential equations, and geometric integration. His work includes leading projects like GALA (EU Sixth Framework Programme) and SPIRIT (Research Council of Norway) and serving as Vice Dean of Education at NTNU's Faculty of Information Technology and Electrical Engineering (2012–2017). Education: PhD in Numerical Analysis (1990, NTNU), MSc in Physics and Mathematics (1984, NTNU). Research focuses on numerical methods for differential equations, geometric integration, and mathematical modeling. He has supervised 10 PhD and 31 MSc students. Leadership roles include President of the Norwegian Mathematical Society (2007–2011) and membership in SEFI and ECMI. Research interests span geometric numerical integration, structure-preserving methods, and applications in computational physics and machine learning. His work bridges theoretical mathematics and computational science, emphasizing methods that preserve geometric properties of dynamical systems. Key contributions include advancements in Lie group integrators, Kahan discretization, and energy-preserving algorithms. Collaborations with institutions like SINTEF and international projects highlight his interdisciplinary impact. Current projects explore neural networks’ integration with differential equations and scientific computing.

Snorre Harald Christiansenمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Computational Mathematics
• +۷ مورد دیگر

Snorre Harald Christiansen is a Professor at the Department of Mathematics , University of Oslo , specializing in Numerical Analysis for Partial Differential Equations . His work bridges Finite Element Methods , Lattice Gauge Theory , and Regge Calculus to develop structure-preserving discretizations for physical field theories. Research Focus : Geometric properties in PDEs, Lie groups, de Rham complexes, elasticity, electromagnetism, and fluid dynamics. Education : Ingénieur (1997) and PhD (2002) from École Polytechnique. Academic Timeline : Associate Professor (2006), Professor (2009) at UiO. His publications emphasize finite element systems for preserving discrete structures, with notable contributions to Calderon preconditioners and dual finite element complexes . Recent works address curvature in Regge calculus, elasticity complexes, and gauge-invariant discretizations. Scientific Awards : EURYI Award (2005) Stephen Smale Prize (2011) ERC Starting Grant (2011) Professor Ingerid Dal Prize (2012) Royal Norwegian Society of Sciences and Letters (2022) He has held editorial positions at SIAM Journal on Numerical Analysis , IMA Journal on Numerical Analysis , and JoFoCM . His teaching includes courses on Partial Differential Equations and Mathematical Structures .

• NMR Spectroscopy
• Enzymology
• Polysaccharide Chemistry
• +۳ مورد دیگر

Finn Lillelund Aachmann is a Professor in the Department of Biotechnology and Food Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Natural Sciences. His research focuses on NMR spectroscopy of biological macromolecules, particularly enzymes involved in polysaccharide degradation and modification. He leads the Biopolymer NMR Lab, exploring applications in biomaterials derived from seaweed and other biological sources. Research interests include lytic polysaccharide monooxygenases (LPMOs), carbohydrate-binding modules (CBMs), and alginate-modifying enzymes. His work integrates biochemical, biophysical, and structural approaches to understand enzyme mechanisms and develop novel biomaterials. Key areas of study involve algal polysaccharides such as alginate, laminarin, and fucoidan, with applications in food science, biotechnology, and biomedical materials. Recent publications highlight advancements in alginate extraction processes, enzymatic modification techniques, and structural elucidation of polysaccharides. His team collaborates on projects like seaweed biorefinery platforms and the functionalization of alginate-based hydrogels. Aachmann advises numerous graduate students, including Kari Hjelen, Øystein Arlov, and Katharina Nøkling-Eide, whose theses focus on enzyme engineering, polysaccharide characterization, and bioprocess optimization. Notable projects include the development of fluorinated probes for enzyme binding studies and the exploration of diatom evolution through horizontal gene transfer. His lab also investigates immunomodulatory properties of fucoidans and the mechanical performance of alginate-composite materials. These efforts bridge fundamental enzymology with applied biotechnology, addressing challenges in sustainability and material innovation.