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Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

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.

• Model Order Reduction
• Scientific Machine Learning
• Fast Simulation of Large-Scale Complex Systems
• +3 more

Dr. Lihong Feng is a Senior Scientist and Team Leader at the Department of Computational Methods in Systems and Control Theory within the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Her research focuses on model order reduction , scientific machine learning , and numerical analysis for efficient simulation of large-scale systems. She obtained her PhD in Computational Mathematics from Fudan University, China, and has held research positions at institutions including University of Freiburg and Chemnitz University of Technology. Research interests: Dr. Feng specializes in computational methods enabling fast simulation of complex technical systems. Her work bridges numerical analysis, machine learning, and control theory to develop dimensionality reduction techniques for industrial applications. She has published extensively on these topics while leading her research team in Magdeburg. Model Order Reduction Scientific Machine Learning Numerical Analysis Applied Mathematics Scientific awards: Alexander von Humboldt Foundation Fellowship Dr. Feng's career spans institutions in Germany and China, with postdoctoral and lecturing experience at Fudan University before joining the Max Planck Institute in 2010. She has advised numerous researchers through her team leadership role and maintains international academic collaborations.

Eric SonnendrückerView profile

Professor

• Numerical Methods
• Plasma Physics
• Computational Physics
• +7 more

Eric Sonnendrücker is a Professor at the Technical University of Munich (TUM), affiliated with the Department of Mathematics in the TUM School of Computation, Information and Technology. He is also a scientific member and director at the Max Planck Institute for Plasma Physics in Garching, reflecting his dual leadership in academia and fundamental research. His work bridges advanced numerical analysis with plasma physics, particularly for magnetic fusion applications. Born in 1967 PhD from École Normale Supérieure de Cachan, France Postdoctoral research at Karlsruhe Research Center and Lawrence Berkeley National Laboratory Professor at University of Strasbourg (2000–2012) Scientific Member and Director, Max Planck Institute for Plasma Physics (since 2012) Professor, TUM (since 2012) His research focuses on the development and analysis of numerical methods for plasma physics, especially kinetic and fluid models such as Vlasov and MHD systems. He specializes in semi-Lagrangian and Particle-in-Cell (PIC) methods, with a strong emphasis on structure-preserving algorithms, geometric integration, and high-performance computing for gyrokinetic simulations in fusion devices like tokamaks and stellarators. The recent publications highlight a consistent trend in advancing geometric and energy-conserving particle-in-cell methods, with applications to electromagnetic gyrokinetic simulations, curvilinear coordinate systems, and large-scale fusion modeling. His work integrates deep mathematical rigor with practical computational challenges in fusion energy research. While no specific awards are listed in the provided texts, his leadership roles and extensive publication record in top journals indicate high recognition in the field. He leads the 'Numerical Methods in Plasma Physics' research group at TUM, which collaborates closely with the Max Planck Institute. The group develops fast, scalable software for high-performance computers and visualization tools for large datasets, focusing on the implementation and analysis of numerical schemes for complex plasma behavior.

• Mathematical Modelling
• Applied Analysis
• differential equations
• +6 more

Prof. Johannes Zimmer is Chair of Analysis and Modelling at the Department of Mathematics, School of Computation, Information and Technology at the Technical University of Munich (TUM). His research focuses on mathematical modeling and analysis of complex systems, with particular expertise in differential equations, variational problems, stochastic models, and nonequilibrium dynamics. His primary research interests include: Mathematical Modelling of physical systems Applied Analysis of differential equations and variational problems Stochastic models and scale-bridging techniques Nonequilibrium problems in statistical mechanics Hydrodynamic limits and fluctuations in particle systems Prof. Zimmer's recent work demonstrates a strong focus on connecting microscopic particle systems to macroscopic evolution equations, with applications in statistical physics, thermodynamics, and multiscale modeling. His research often combines rigorous mathematical analysis with physical insights from nonequilibrium statistical mechanics. A notable trend in his recent publications is the development of frameworks that bridge Hamiltonian dynamics with dissipative structures, as well as the application of machine learning techniques to coarse-graining problems in statistical physics. Among his scientific contributions are advancements in understanding: GENERIC formalism for non-equilibrium thermodynamics Dean-Kawasaki models for density fluctuations Fast-slow Hamiltonian systems and their thermodynamic interpretation Statistical-physics-informed neural networks Prof. Zimmer actively supervises students and collaborates with researchers across disciplines, including physicists, applied mathematicians, and computational scientists. His work often involves international collaborations with institutions in Europe and beyond. He teaches advanced mathematics courses at TUM, including Higher Mathematics for Mechanical and Chemical Engineering students, and leads seminars on mathematical modeling and nonequilibrium systems.

Michele NottoliView profile

Researcher

• Computational Chemistry
• Quantum Mechanics
• Molecular Dynamics
• +5 more

Michele Nottoli is a researcher at the University of Stuttgart's Institute of Applied Analysis and Numerical Simulation (Faculty 08), specializing in Numerical Mathematics for High-Performance Computing. He holds a Ph.D. and works as a Postdoc and Research Assistant. His research focuses on developing advanced computational methods for quantum mechanics and molecular dynamics, emphasizing polarizable embedding models, QM/MM coupling, and high-performance numerical algorithms. Key contributions include the development of the ddX library for polarizable continuum solvation and optimizing linear-scaling methods for large-scale molecular simulations. Nottoli's work addresses challenges in modeling light-driven processes in complex systems, such as photoreceptors and antenna complexes, with applications in biochemistry and materials science. His methodologies integrate domain-decomposition techniques, fast multipole methods, and symmetry-preserving approaches to enhance computational efficiency and accuracy. He actively contributes to open-source software development, including the OpenMMPol library, to facilitate multiscale simulations of embedded molecules in polarizable environments. His research also explores excited-state dynamics, solvation effects, and nonperiodic boundary conditions, with implications for understanding magnetic shifts and optical properties in chemical systems. Nottoli collaborates on projects involving light-harvesting mechanisms in purple bacteria and the molecular mechanisms of solvent shifts in nitroxide radicals. Despite his prolific publication record, no scientific awards are currently listed. Advising and grants: Nottoli is actively involved in postdoctoral research but no specific advisees or grant details are provided in the available information. His affiliation with the Numerical Mathematics group positions him at the intersection of algorithm design and applied computational science.

Frank NeeseView profile

Professor

• Theoretical Chemistry
• Quantum Chemistry
• Molecular Spectroscopy
• +4 more

Prof. Frank Neese is the Managing Director of the Max Planck Institute for Coal Research and Honorary Professor at the University of Duisburg-Essen. His research focuses on molecular theory, quantum chemistry, and spectroscopy, with a strong emphasis on developing computational methods (e.g., the ORCA software) and their application to catalysis, electronic structure analysis, and transition metal systems. He holds numerous awards, including the Leibniz Prize and membership in prestigious academies. Education: Ph.D. (1997) and Habilitation (2001) in Bioinorganic Chemistry from the University of Konstanz. Postdoc at Stanford University (1997–1999). Previously served as Director of the Max Planck Institute for Chemical Energy Conversion (2011–2017) and Full Professor at the University of Bonn (2006–2011). Research interests include quantum chemical method development, computational studies of catalytic reactions (e.g., water oxidation in photosystem II), and advanced spectroscopic techniques like X-ray absorption/emission and Mößbauer spectroscopy. His ORCA software is widely used globally. Key contributions span over 600 publications, including work on correlated wavefunction methods (DLPNO-CCSD(T)), molecular magnetism, and heterogeneous catalysis. Awards include the Coulson Lectureship (2016), Davidson Lectureship (2015), and Leopoldina membership (2013). Grants and collaborations involve leadership in the Collaborative Research Centre TRR247 (catalysis) and international advisory roles (e.g., Faraday Discussions, GDCh committees). His lab hosts PhD students and postdocs in computational chemistry, spectroscopy, and materials science.

• Big Data Analytics
• Particle Physics
• Deep Learning
• +7 more

Johannes Erdmann is a University Professor and leads the Research and Teaching Area of Big Data Analytics in Physics at RWTH Aachen University. He is affiliated with the Department of Physics within the Faculty of Mathematics, Computer Science and Natural Sciences. His research integrates advanced data science techniques with fundamental physics investigations. His research focuses on leveraging big data and machine learning methods to solve complex problems in particle and astroparticle physics. Key areas include the physics of the Higgs boson, data analysis at the CMS experiment at CERN, and the development of deep learning techniques for high energy physics. He also contributes to gravitational wave physics, particularly in noise suppression strategies for the future Einstein Telescope. The recent publications highlight a strong trend toward methodological innovation, especially in deep learning applications for particle physics simulations and data analysis. His team is actively developing novel neural network architectures, fast simulation tools, and automated quality assurance systems, all aimed at enhancing the precision and efficiency of large-scale physics experiments. Heisenberg-Professur funded by the Deutsche Forschungsgemeinschaft (DFG) Prof. Erdmann supervises research projects and leads a research group focused on data-intensive physics. He is actively involved in securing and managing research grants, particularly those supporting computational and methodological developments in experimental physics. His group collaborates with international teams at CERN and within the Einstein Telescope consortium. His research group, part of the Big Data Analytics in Physics unit, works on cutting-edge challenges in data processing for particle and gravitational wave physics. The team develops deep learning models, simulation frameworks, and data quality tools, contributing to major international collaborations such as CMS and the Einstein Telescope project.