Arne Hildenbrand

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.