Find Academic Supervisors

• Structural Analysis
• Computational Mechanics
• Digital Twin Technology
• +4 more

Talhah Shamshad Ali Ansari is a Research Associate at the Chair of Structural Analysis and Dynamics at the Technical University of Munich (TUM) . He works on advanced computational methods, focusing on digital twins, adjoint-based system identification, and multiphysics simulations. His research addresses structural optimization, wind engineering, and robust meshing techniques. Research Highlights : Digital Twin technology for structural analysis Adjoint-based methods for system identification Multiphysics simulations in wind engineering Structural optimization for additive manufacturing Teaching : Contributed to courses in Theory of Plates and computational mechanics curricula Publications (2025): Developed adjoint-based thermal field recovery methods Analyzed algorithms for digital twin system identification Advanced high-fidelity simulations for structural weaknesses

Simon AdrianView profile

Professor

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +3 more

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

Paul MannsView profile

Assistant Professor

• Mathematical Optimization
• PDE-Constrained Optimization
• Integer Programming
• +1 more

Paul Manns is an Assistant Professor of Optimization at TU Dortmund University's Department of Mathematics, appointed in 2021. His research specializes in mathematical optimization involving partial differential equations and integer constraints, with emphasis on regularization techniques and trust-region algorithms. Education includes: Ph.D. in Mathematics, TU Braunschweig (2019) Computational Engineering studies, TU Darmstadt Prior research experience includes positions at Heidelberg University, TU Braunschweig, and Argonne National Laboratory (USA), including a James H Wilkinson Fellowship. Recent publications develop novel methods for mixed-integer control problems, domain decomposition, and convergence analysis in non-convex optimization spaces.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.

Michael Schlottke-LakemperView profile

Professor

• High-Performance Scientific Computing
• Adaptive Multi-Physics Simulations
• Research Software Engineering
• +4 more

Dr. Michael Schlottke-Lakemper is a Professor of High-Performance Scientific Computing at the University of Augsburg, Faculty of Mathematics, Natural Sciences, and Materials Engineering. He previously held positions as an Interim Professor of Computational Mathematics at RWTH Aachen University (2022–2024) and led a research group at the High-Performance Computing Center Stuttgart (HLRS) from 2021 to 2024. His career includes postdoctoral roles at the University of Cologne and RWTH Aachen University/FZ Jülich. Education: Ph.D. in Mechanical Engineering, RWTH Aachen University (2017) Diplom in Aerospace Engineering, University of Stuttgart (2011) His research focuses on adaptive multi-physics simulations, research software engineering for high-performance computing (HPC), and scientific machine learning. Applications span fluid mechanics, aeroacoustics, and astrophysics, with recent work emphasizing robust high-order summation-by-parts methods and Julia-based computational frameworks like Trixi.jl and TrixiParticles.jl. His publications highlight advancements in discontinuous Galerkin methods, entropy stable schemes, and HPC optimization for compressible flows. Scientific contributions include Developing dynamic load balancing algorithms for multiphysics simulations Creating hybrid computational aeroacoustics methods Advancing Julia's adoption in HPC communities Improving error-based step size control in numerical solvers Current teaching activities include graduate seminars on Maschinelles Lernen in Theorie und Praxis and undergraduate courses in Numerische Lineare Algebra . He leads a research team at the University of Augsburg with collaborators across Germany, including Simon Candelaresi, Valentin Churavy, and Niklas Neher.

• Thermoacoustics
• Combustion Dynamics
• Flame Modeling
• +5 more

Camilo Fernando Silva Garzon is a Privatdozent (PD) and academic researcher at the Department of Thermofluid Dynamics at the Technical University of Munich (TUM). He holds the title of Dr. habil., indicating he has completed his habilitation, the highest academic qualification in the German system. Working under the supervision of Prof. Wolfgang Polifke, he is actively involved in cutting-edge research in thermoacoustics and combustion dynamics. Dr. Garzon's research spans multiple areas of thermo-fluid dynamics with a particular focus on thermoacoustic instabilities, flame modeling, and combustion dynamics. His work combines theoretical approaches with computational methods to address complex problems in energy systems. He has developed innovative techniques for modeling flame response, thermoacoustic interactions, and entropy wave generation in combustion systems. His research has significant applications in gas turbine design, aero-engine development, and sustainable combustion technologies. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional combustion modeling approaches. His work increasingly focuses on uncertainty quantification, nonlinear dynamics, and the development of reduced-order models for complex thermo-fluid systems. He has made significant contributions to understanding intrinsic thermoacoustic instabilities and developing new methodologies for flame response modeling. His research shows a clear progression from fundamental theoretical work toward practical applications in gas turbine and aero-engine technologies. Dr. Garzon has established a robust collaborative network, frequently publishing with researchers from TUM and international institutions. His work appears in top journals including Combustion and Flame, Journal of Engineering for Gas Turbines and Power, and Proceedings of the Combustion Institute. He has contributed significantly to advancing the understanding of thermoacoustic phenomena and combustion dynamics through both theoretical developments and practical applications.

Thomas WickView profile

Professor

• Numerical Analysis
• Computational Mechanics
• Partial Differential Equations
• +3 more

Prof. Dr. Thomas Wick is a Professor at the Institute of Applied Mathematics within the Faculty of Mathematics and Physics at Leibniz University Hannover. He holds leadership roles, including Executive Director of the Institute and membership in the Executive Board and Faculty Council. His research focuses on numerical methods for coupled nonlinear partial differential equations, multiphysics systems (e.g., fluid-structure interaction, phase-field fracture), adaptive finite element techniques, and robust solvers. Key projects include the DFG-funded SPP 1962 and SPP 1748 initiatives, the PhoenixD Cluster of Excellence, and international collaborations like the Indo-German Higher Education Partnership. He has received grants from DFG, DAAD, and the Alexander von Humboldt Foundation. His work emphasizes algorithm design, error control, and computational efficiency in engineering and scientific applications. Research Interests: Numerical modeling of coupled PDE systems, multiphysics phenomena, phase-field fracture, adaptivity, and optimization. Projects include CoMeTeNd (IRTG 2657/1), PhoenixD Task Group S4, and Strukturerhaltende Adaptive Enriched Galerkin Methods. Scientific Awards: Feodor Lynen Fellowship, DFG Projects, DAAD grants. Collaborations span Germany, Austria, India, Peru, and France. Publications highlight advancements in phase-field fracture, fluid-structure interaction, and adaptive methods. His contributions address challenges in mesh adaptivity, error estimation, and high-performance computing.

Paul SteinmannView profile

Professor

• Engineering Mechanics
• Solid Mechanics
• Materials Modeling
• +7 more

Prof. Dr. Paul Steinmann is a full Professor and Chair of Engineering Mechanics (LTM) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Faculty of Engineering. Since December 2020, he serves as Special Representative for Strategic Development at FAU, supporting academic positioning and research excellence. He is also Director of the Glasgow Computational Engineering Centre (GCEC), UK, reflecting his international leadership. Research Interests: His work focuses on advanced computational and theoretical mechanics, including materials modeling, multiscale and multiphysics problems, fracture mechanics, biomechanics, and finite element methods. He develops robust mathematical and numerical frameworks for complex material behavior under extreme conditions. Publication Trends: His recent research emphasizes cross-scale fracture modeling, phase-field methods, thermomechanical coupling, chemo-mechanical degradation, and configurational mechanics. These reflect a strong trend toward unified, thermodynamically consistent frameworks applicable across engineering and biological systems. Scientific Awards: Fellow Award from IACM (2006) Fellow Award from EUROMECH (2006) Timoshenko Visiting Scholar Award, Stanford University (2007) ERC Advanced Grant (2011) Royal Society Wolfson Research Merit Award (2017) Advising and Grants: As spokesperson for the DFG Research Training Group FRASCAL and former leader of multiple DFG projects, he has supervised numerous PhD students and postdocs. He has secured major grants including the ERC Advanced Grant and led collaborative research across Germany and internationally. He has served on DFG and DAAD committees, contributing to national research policy and funding decisions. Labs and Teams: He leads the Chair of Engineering Mechanics at FAU and is deeply involved in the DFG Research Training Group FRASCAL, fostering interdisciplinary research across mechanics, materials science, mathematics, chemistry, and physics. His international role as Director of GCEC strengthens computational engineering collaborations between FAU and UK institutions.

Sebastian SchöpsView profile

Associate Professor

• Computational Electromagnetics
• Coupled Multiphysical Simulations
• High-Performance Computing
• +7 more

Prof. Sebastian Schöps is an Associate Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. He leads the Computational Engineering Group, focusing on coupled multiphysical simulations and high-performance computing. His work bridges computational electromagnetics with advanced numerical methods like isogeometric analysis and reduced-order modeling. Education: Joint PhD in Physics (KU Leuven, Belgium) and Mathematics (University of Wuppertal) MSc and BSc in Business Mathematics (University of Wuppertal) Research Interests: Coupled systems involving electromagnetism, thermodynamics, and structural mechanics High-performance parallel algorithms for industrial-scale simulations Uncertainty quantification in engineered systems Optimization of electric machines using isogeometric analysis His work frequently involves developing novel finite element formulations and collaborating with CERN on accelerator magnet simulations. Publications Trends: Recent work emphasizes machine learning integration with physics-based models, optimization under uncertainty, and scalable simulation techniques for superconducting devices. Key applications include electric machine design, particle accelerator magnets, and medical device modeling. Labs & Projects: Leads the PASIROM project (Parallel Simulation and Robust Optimization of Electro-Mechanical Energy Converters), and contributes to CERN's quench protection system research. Active in open-source tool development for multiphysics simulation.

Alfred KamusellaView profile

Researcher

• Optimization
• Computer-Aided Design
• Mechatronics
• +3 more

Alfred Kamusella serves as a Researcher at Dresden University of Technology's Institute of Precision Engineering and Electronic Design, working as a Research Associate since May 2021 on a freelance basis. His responsibilities include research in mechatronic systems modeling and computer-aided design, alongside previously teaching CAD, Finite Element Method, and device technology courses through the 2020/2021 academic year. His research centers on probabilistic simulation and multi-criteria optimization for early-phase computer-aided design of mechatronic systems. He develops numerical modeling, simulation, and optimization methods for device analysis and synthesis, incorporating laboratory experiments to address design variations and conflicting requirements. Key focus areas include reliability engineering, finite element analysis, and electromagnetic actuator optimization. Publications from 2006-2011 reveal consistent specialization in robust design optimization using probabilistic methods, particularly for electromagnetic actuators and microsystems. His work integrates COMSOL Multiphysics, OptiY software, and multi-domain simulation techniques to enhance reliability assessment in mechatronic systems under parameter uncertainties. Kamusella administers the OptiYummy.de portal providing optimization tutorials and foundational resources. No information is available regarding student advising, grant funding, or scientific awards in the provided documentation.

• Wind Engineering
• Isogeometric Analysis
• Structural Optimization
• +6 more

Muhamed Horo is a Research Associate at the Chair of Structural Analysis at Technical University of Munich since 2024. He holds an M.Sc. in Civil Engineering from Technical University of Munich (2021–2024) and a B.Sc. in Civil Engineering from Middle East Technical University (2013–2019). Research Interests: Wind Engineering (modelling/simulation, vibrations, flexible membrane wings) Isogeometric Analysis (B-Rep elements, design of membrane structures) Structural Optimization (node-based approaches, additive manufacturing in construction) Multiphysics and Fluid-Structure Interaction Mesh regularization and robust mesh-moving techniques Smart structures and lightweight design Third-Party Projects: Involved in CoDA, Mistralwind, WINSENT, FlexWing, Digital Building Kit, and Generalized Force Density Method. Software Expertise: Carat++, Kratos Multiphysics, Windgenerator, TeDA, Kiwi!3D.

Christine SchwarzView profile

Lecturer

• Topology optimization of structures
• Multifidelity shape optimization
• Multiphysics robustness analysis
• +2 more

Christine Schwarz is a Lecturer at the Chair of Land Management and Rural Development at the Technical University of Munich (TUM). Her research focuses include topology optimization for nonlinear collision processes, multifidelity shape optimization, multiphysics robustness analysis, finite element modeling for injury minimization, and multi-level material modeling for fiber composites. Department: Chair of Land Management and Rural Development University: Technical University of Munich Email: christine.schwarz@tum.de

• Machine Learning
• Surrogate Modeling
• Data Augmentation
• +5 more

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
• +4 more

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.

• Wind Engineering
• Uncertainty Quantification
• Structural Optimization
• +5 more

Anoop Kodakkal is a Researcher at the Chair of Statics and Dynamics of the Technical University of Munich , where he has worked since 2024. He was previously a Research Associate at the Chair of Structural Analysis (2020-2024) and completed his doctoral studies (Dr.-Ing.) at TUM as a DAAD scholarship holder (2016-2020). He holds an M.Tech in Structural Engineering from IIT Delhi (2012-2014) and a B.Tech in Civil Engineering from NIT Calicut (2008-2012). Research Interests: Wind Engineering Uncertainty Quantification (UQ) Fluid-Structure Interaction Structural Optimization Isogeometric Analysis Machine Learning in Structural Dynamics Scientific Awards: DAAD Doctoral Research Grant (2016-2020) DAAD IIT Master Sandwich Scholarship (2013-2014) Recent Publications focus on wind-induced vibrations, uncertainty quantification, and machine learning integration with finite element analysis. Notable trends include: Optimization under uncertain wind conditions Development of neural networks for structural dynamics Standardization of membrane roof design Multi-fidelity modeling for turbulent flows Stochastic analysis of nonlinear systems Computational tools for wind engineering