Nikolaus Adams

Nikolaus Adams is a Professor at the Chair of Aerodynamics and Fluid Mechanics at the Technical University of Munich (TUM). His research focuses on computational fluid dynamics (CFD), numerical methods, and data-driven modeling of complex fluid phenomena. Key contributions include the development of differentiable CFD frameworks like JAX-Fluids and integration of machine learning with high-order schemes.

Research Interests:

• High-order numerical methods (WENO, SPH, Lattice-Boltzmann)
• Machine learning/RL for flow control and turbulence modeling
• Quantum algorithms for fluid simulations
• Multiphase flows with surface tension and cavitation
• Shock wave interactions and aerodynamic breakup

Recent article trends highlight applications of differentiable programming, neural networks, and Bayesian optimization in compressible/two-phase flows, alongside quantum lattice-Boltzmann advancements.

Labs/Teams: Leads the Chair of Aerodynamics and Fluid Mechanics at TUM, contributing to the TUMWAER group and TUZEMSE research initiatives.