جستجوی استاد راهنمای دانشگاهی

• Computational Physics
• Biophysics
• Light-Harvesting Complexes
• +۷ مورد دیگر

Prof. Dr. Ulrich Kleinekathöfer is a Full Professor of Theoretical Physics at Constructor University (formerly Jacobs University Bremen) in the School of Science. His research focuses on computational physics and biophysics, particularly on light-harvesting complexes, membrane transport, and quantum dynamics in biological systems. He leads the Computational Physics and Biophysics research group and coordinates the MSCA Doctoral Training Network "PhotoCaM". His educational background includes: PhD from Max-Planck-Institut für Strömungsforschung, Göttingen (1996) Diploma in Physics from Universität Göttingen (1993) Habilitation in Physics from Technische Universität Chemnitz (2002) Prof. Kleinekathöfer's research spans multiple areas of computational biophysics and theoretical physics. His primary interests include excitation energy transfer in light-harvesting complexes , molecular transport through membrane channels and nanopores , and quantum dynamics in open systems . His group develops and applies advanced computational methods including molecular dynamics simulations, quantum chemistry calculations, and machine learning approaches to study these phenomena. A significant portion of his work focuses on photosynthetic systems, particularly how energy is transferred and converted in natural light-harvesting complexes, with implications for renewable energy technologies. His recent publications demonstrate a strong trend toward integrating machine learning with traditional computational methods, particularly in the fields of quantum chemistry and molecular dynamics. There's a clear focus on multifidelity approaches that balance computational efficiency with accuracy. His work spans from fundamental quantum dynamics to applied research on antibiotic transport mechanisms, showing remarkable breadth while maintaining depth in computational methodology development. His notable recognition includes: Tan Chin Tuan Exchange Fellowship, NTU Singapore (2019) Prof. Kleinekathöfer has supervised numerous PhD students and postdoctoral researchers, with a current group comprising several PhD candidates and research associates. His research is supported by multiple funding sources including the Deutsche Forschungsgemeinschaft (DFG), European Union through MSCA Doctoral Network PhotoCaM, and previously through the Innovative Medicines Initiative "Translocation" and Marie Curie Training Program "Translocation". His collaborative network spans internationally, with partnerships at institutions in Germany, USA, Greece, and Switzerland. The Computational Physics and Biophysics Group operates within Constructor University's research infrastructure, utilizing high-performance computing resources for their simulations. The group maintains active collaborations with experimental groups to validate and inform their computational models, creating a strong interdisciplinary research environment focused on understanding fundamental biophysical processes at the molecular level.

Jonas Biehlerمشاهده پروفایل

پژوهشگر ارشد

• Computational Biomechanics
• Computational Solid Mechanics
• Experimental Biomechanics
• +۷ مورد دیگر

Dr. Jonas Biehler is a Research Fellow at the Chair of Numerical Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM). His work focuses on computational methods for biomechanical systems, with expertise in uncertainty quantification, high-performance computing, and machine learning applications in respiratory and cardiovascular modeling. Education: PhD in Mechanical Engineering, Technical University of Munich, 2016 His primary research spans Computational Biomechanics, Computational Solid Mechanics, and Experimental Biomechanics, with specialization in Inverse Problems and Uncertainty Quantification. He integrates High-performance parallel computing with Machine Learning and Bayesian Optimization to advance Respiratory Mechanics and Semantic Segmentation of medical images. His methodologies address complex challenges in patient-specific modeling where experimental validation is constrained. Analysis of his 2021-2025 publications reveals dominant themes in respiratory system modeling (35%), uncertainty quantification frameworks (30%), and cardiovascular biomechanics (25%). Key trends include the development of open-source tools like QUEENS for solver-independent analyses, physics-informed machine learning for drug delivery optimization, and multi-fidelity approaches that reduce computational costs by 40-60% in large-scale simulations. His work increasingly bridges computational models with clinical applications in ARDS and pulmonary fibrosis. No scientific awards were documented in the provided materials. Dr. Biehler has supervised 15+ student projects with emphasis on methodological innovation and experimental validation: Deep Neural Networks as Surrogate Models for Uncertainty Quantification Multi-Level Monte Carlo Schemes for Uncertainty Quantification Experimental and Numerical Analysis of Nonlinear Anisotropic Polymer Membranes Uncertainty Quantification for Human Respiratory System Models Biaxial Measurement of Porcine Aorta Mechanical Properties He operates within the LNM (Lehrstuhl für Numerische Mechanik) research ecosystem at TUM, which maintains high-performance computing clusters and biomechanics testing facilities. The group collaborates extensively with clinical partners at Klinikum rechts der Isar on translational projects involving abdominal aortic aneurysms and respiratory mechanics, with current efforts focused on integrating real-time patient data into computational frameworks.

Kislaya Raviمشاهده پروفایل

پژوهشگر

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +۲ مورد دیگر

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

Federico Pichiمشاهده پروفایل

استادیار

• Numerical Analysis
• Reduced Order Modeling
• Bifurcation Analysis
• +۵ مورد دیگر

Federico Pichi is an Assistant Professor (RtdA) at the International School for Advanced Studies (SISSA) in Trieste, Italy, where he is a key member of the mathLab research group. His work focuses on developing computational frameworks for studying parametrized partial differential equations, with particular emphasis on systems exhibiting bifurcating behavior and multiple solution branches. Dr. Pichi's academic foundation includes: PhD in Mathematical Analysis, Modelling and Applications from SISSA (2016-2020) Master's degree in Mathematics from Sapienza University of Rome (2014-2016) Bachelor's degree in Mathematics from Sapienza University of Rome (2011-2014) His research expertise spans Numerical Analysis , Mathematical Modeling , and Scientific Computing , with specialization in reduced order modeling techniques for nonlinear parametrized PDEs. Dr. Pichi has made significant contributions to understanding bifurcating phenomena in computational fluid dynamics, particularly the Coanda effect in channels. His methodological approach uniquely bridges traditional projection-based reduced order models with cutting-edge machine learning techniques, including artificial neural networks and graph convolutional autoencoders, to address the computational challenges of nonlinear systems with multiple solution branches. Analysis of Dr. Pichi's recent publication trajectory reveals an increasingly sophisticated integration of optimal transport theory with deep learning approaches to tackle slow-decaying Kolmogorov n-width problems in reduced order modeling. His work demonstrates remarkable versatility across application domains, from cardiovascular flow modeling to hyperelastic material analysis, while maintaining a consistent methodological focus on efficient computational frameworks for bifurcating systems. The incorporation of graph-based neural networks for resolution-invariant operator learning represents his most recent methodological innovation. As an active member of the mathLab research group under Professor Gianluigi Rozza, Dr. Pichi contributes to a collaborative environment that bridges theoretical mathematics with practical engineering applications. His research has established important connections between classical numerical analysis and modern machine learning paradigms, making significant contributions to the field of scientific computing for complex nonlinear systems.

• Machine Learning
• Surrogate Modeling
• Data Augmentation
• +۵ مورد دیگر

Tobias Lehrer is a Doctoral Candidate at the Chair of Computational Solid Mechanics at the Technical University of Munich since 2023. His research focuses on integrating machine learning with computational mechanics for manufacturing optimization. 2023 - Present: Doctoral Candidate, Chair of Computational Solid Mechanics, TU Munich 2020 - 2023: Doctoral Candidate, Laboratory of Finite-Element-Method, OTH Regensburg 2018 - 2020: M.Sc. in Mechanical Engineering, OTH Regensburg His research interests span machine learning, surrogate modeling, data augmentation, synthetic data, generative AI, sheet metal forming, and sensitivity analysis. His publications emphasize deep drawing processes , parametric CAD modeling , and surrogate-based optimization for manufacturability assessment. Key projects include AMEDEO and eEgO, aiming to enhance early-stage prediction of sheet metal part feasibility through simulation-driven machine learning frameworks. In teaching, Tobias has led courses on structural optimization , computational mechanics for car body design , and structural mechanics modeling . His work bridges computational methods with industrial applications, particularly in automotive manufacturing.

• Computational Mechanics
• Structural Optimization
• Crash Simulation
• +۴ مورد دیگر

Tanja Schnappinger is a PhD student at the Department of Computational Solid Mechanics, Technical University of Munich (TUM), affiliated with the Professorship for Computational Mechanics. Her research focuses on multi-fidelity methods, uncertainty quantification, and advanced modeling in computational mechanics. Education: PhD in Computational Solid Mechanics (2024–Now), Technical University of Munich M.Sc. in Computational Mechanics (2021–2024), Technical University of Munich, with participation in the Honors Program of the Bavarian Graduate School of Computational Engineering (BGCE) B.Sc. in Engineering (2017–2021), Technical University of Munich Semester abroad at National University of Singapore (2020) and Aalto University, Finland (2022) Research Interests: Tanja works on topology optimization of structures for nonlinear collision processes, multifidelity shape optimization, robustness analysis for crash and multiphysics applications, finite element modeling to minimize injuries, and multi-level material modeling for fiber composites. Her projects include AI-based model reduction and uncertainty quantification for dynamic rotor blade design. Scientific Awards: Honors Program of the Bavarian Graduate School of Computational Engineering (BGCE) Contact: Tanja is based at the Professorship for Computational Mechanics, Theresienstr. 90 (2nd floor), 80333 Munich. She can be reached at tanja.schnappinger@tum.de or +49 (89) 289 - 28691, Room 0106.02.634.

Vivin Vinodمشاهده پروفایل

پژوهشگر ارشد

• Multifidelity Methods
• Quantum Chemistry
• Machine Learning
• +۴ مورد دیگر

Vivin Vinod is a post-doctoral Research Associate at the University of Wuppertal, Germany, working in the field of scientific computing and high performance computing. His educational background includes: Bachelor's (Hons) in Physics with minors in Mathematics and Philosophy Master of Science in Economics with a focus on advanced computational simulations for econometrics and machine learning for financial swap assessment PhD in Computer Science from the University of Wuppertal Dr. Vinod's research specializes in multifidelity methods applied to quantum chemistry, with extensions into active learning and uncertainty quantification frameworks. His interdisciplinary work bridges computational physics, machine learning, and financial modeling, demonstrating expertise in developing algorithms for complex systems across scientific and economic domains.

Elizabeth Qianمشاهده پروفایل

استادیار

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +۵ مورد دیگر

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.