Johannes Schmid

Johannes Schmid, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich. His work focuses on integrating machine learning with computational acoustics, particularly in physics-informed deep learning and uncertainty quantification for vibroacoustic systems.

Research Interests

• Physics-informed deep learning for acoustic modeling
• Data-driven surrogate modeling for dynamic systems
• Uncertainty quantification in industrial applications
• Stochastic Finite Element Methods
• Interactive acoustics apps for education

Publications

Schmid has authored/co-authored over 15 publications in computational acoustics, with recent work on neural networks for boundary integral methods, metamaterial design, and hybrid machine learning techniques. His research spans automotive applications, noise control, and educational tools.

Laboratory Affiliation

He is affiliated with the Chair of Vibroacoustics of Vehicles and Machines, contributing to projects on vehicle acoustics, fluid-structure interaction, and deep learning applications in engineering.