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Prof. Robert Meißner is a Professor at the Department of Surface Physics and Technology at TUHH. His research focuses on molecular simulation techniques applied to corrosion processes, energy storage systems, and nanomaterials. He develops computational tools like ELECTRODE and i-PI for electrochemical and advanced molecular dynamics simulations. His work addresses challenges in magnesium battery performance, structural health monitoring of composite materials, and interfacial phenomena in nanoscale systems. Education details are not explicitly provided in the text, but his professional trajectory reflects extensive academic and industrial experience in materials science. Research interests span from fundamental studies (e.g., water imbibition in nanopores, magnetite oxidation dynamics) to applied innovations (e.g., corrosion protection via layered double hydroxides, data-driven electrolyte design). His recent publications highlight trends in data-driven materials discovery, structural health monitoring via vibro-acoustic methods, and computational prediction of corrosion inhibitors. He collaborates on projects involving graphene-based supercapacitors, epoxy resin curing dynamics, and peptide-surface interactions. Advising and grants: While student names are not listed, his research group actively explores corrosion engineering, battery technology, and nanomaterials. Projects include EU-funded initiatives and industry partnerships. Technical expertise includes ATR-FTIR spectroscopy, molecular dynamics, and machine learning for sparse data scenarios. He leads teams focused on surface science and energy storage, maintaining lab facilities for in situ electrochemical analysis and advanced computational modeling. His work bridges theoretical insights with practical applications in materials durability and energy systems.

• Materials Science
• Corrosion Engineering
• Electrochemistry
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Arne Hildenbrand, M.Sc., serves as a Research Associate at the Chair of Vibro-Acoustics of Vehicles and Machines led by Prof. Dr.-Ing. Steffen Marburg at the Technical University of Munich (TUM). He is affiliated with the TUM School of Engineering and Design within the Department of Engineering Physics and Computation. His research focuses on uncertainty quantification and Bayesian/variational inference methods, data-driven deep learning approaches, and Galerkin methods for computational acoustics. His work spans automotive vibroacoustics, particularly in developing variational autoencoder models for dimensionality reduction and admittance response modeling of car bodies. Hildenbrand teaches several courses including Computational Acoustics, Numerical Acoustics in Python, Vibroacoustic Finite Element Simulations in Virtual Prototyping, and Machine Learning in Computational Engineering Mechanics. These courses emphasize practical implementation of numerical methods for acoustic analysis using Python programming. His scientific work demonstrates a strong trend toward integrating machine learning techniques with traditional computational acoustics methods, particularly focusing on variational autoencoders for dimensionality reduction in automotive vibroacoustic applications. The research bridges theoretical acoustics with practical automotive engineering challenges. Hildenbrand collaborates with researchers including Steffen Marburg, Mert Dogu, and Michael Buba on various teaching and research activities within the vibroacoustics laboratory at TUM.

• Uncertainty Quantification
• Bayesian Inference
• Variational Inference
• +5 more

Mert Dogu, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich (TUM School of Engineering and Design, Department of Engineering Physics and Computation). His work focuses on deep learning in acoustics, active vibration control using piezoelectric properties, computational vibro-acoustics, and finite/boundary element methods. Recent research includes interactive acoustics apps for education and active vibration control with integrated piezoelectrics. He contributes to teaching courses such as Computational Acoustics and Numerical Acoustics in Python , emphasizing practical and computational aspects of technical acoustics. His collaborations with Steffen Marburg and colleagues highlight interdisciplinary applications in vehicle acoustics , fluid-structure interaction , and modeling uncertainty . Details about grants, advisors, and awards are not explicitly mentioned in the text.

• Deep Learning in Acoustics
• Active Vibration Control
• Computational Vibro-Acoustics
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Simone Preuss, M.Sc., is a Research Associate at the Chair of Vibro-Acoustics of Vehicles and Machines at the Technical University of Munich (TUM). She is affiliated with the TUM School of Engineering and Design, Department of Engineering Physics and Computation. Her research focuses on advanced computational acoustics techniques, particularly the Boundary Element Method (BEM), viscothermal acoustics, and acoustic metamaterials. Her recent work explores the integration of machine learning with BEM for robust sound field calculations and damping quantification. She has contributed to projects involving fluid-structure interaction, inverse acoustics, and numerical methods in Python. Her publications address challenges in thermoviscous acoustics, fast multipole BEM, and isogeometric analysis. Simone actively teaches courses such as Numerical Acoustics in Python and collaborates with colleagues including Steffen Marburg, Ahmed Mostafa Shaaban, and Johannes Schmid. Her research bridges theoretical advancements in acoustics with practical applications in engineering mechanics and vehicle acoustics.

• Boundary Element Method
• Viscothermal Acoustics
• Acoustic Metamaterials
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Dr. Marinus Luegmair serves as a Guest Lecturer at the Technical University of Munich (TUM) within the Chair of Vibro-Acoustics of Vehicles and Machines under Prof. Steffen Marburg. He specializes in teaching signal processing applications for vibroacoustic systems since 2018. His academic background includes: Mechanical Engineering at University of Applied Sciences Ingolstadt Ph.D. in Electronics and Information Technology at University Magdeburg (thesis: Development of a simulation method for high frequency transient structure-born sound ) Research expertise centers on vibroacoustic simulation and measurement techniques , with emphasis on structural dynamics, vehicle interior acoustics, and uncertainty quantification. His industry work at BMW AG, P+Z Engineering, and ISKO Engineers involved NASTRAN simulations, PAK/Test.Lab measurements, and ray-tracing methodologies for body-in-white development. No scientific awards were documented in the source material. Dr. Luegmair supervises student theses at TUM while maintaining active industry collaboration. His projects focus on industrial vibroacoustic problem-solving without explicit grant mentions. He contributes to TUM's research ecosystem through the Chair of Vibroacoustics, participates in DEGA's vehicle acoustics working group, and presents at international conferences (AAA, ICSV, ISNVH, DAGA).

• Vibroacoustics
• Structural Dynamics
• Signal Processing
• +3 more

Quirin Aumann is a postdoctoral researcher at the Max Planck Institute for Dynamics of Complex Technical Systems , where he focuses on robust model order reduction (MOR) and surrogate modeling for complex dynamical systems. He holds a PhD (2022) and M.Sc. in Computational Mechanics from the Technical University of Munich (TUM) , with earlier B.Sc. training in Civil Engineering at the same institution. Key affiliations : Max Planck Institute for Dynamics of Complex Technical Systems (2022–present) Chair of Structural Mechanics, TUM (2017–2022) Research focus : Preservation of system characteristics in MOR Integration of data-driven methods into engineering workflows Software contributions : Kratos Multiphysics (parallel multi-disciplinary simulation framework) vibro-acoustic-mor (MATLAB-based MOR project) RBF (radial basis function tools) Professional ethos : Advocacy for open data and open-source software Active presence on GitHub, Google Scholar, and ORCID

• Model Order Reduction
• Surrogate Modeling
• Data-driven Methods
• +3 more