Josef Winter

Josef Winter is a researcher at the Department of Aerodynamics and Fluid Mechanics within the TUM School of Engineering and Design at the Technical University of Munich. His work focuses on advanced numerical methods for fluid dynamics simulations, integrating quantum computing, machine learning, and Bayesian optimization to enhance computational efficiency and accuracy.

• University: Technical University of Munich
• School: TUM School of Engineering and Design
• Department: Department of Aerodynamics and Fluid Mechanics
• Academic Rank: Researcher
• Email: josef.winter@tum.de

Dr. Winter's research interests include:

• Quantum algorithms for fluid dynamics
• Multi-fidelity and Bayesian optimization techniques
• High-order numerical methods
• Level-set-based sharp-interface simulations
• Deep reinforcement learning for flow optimization
• Complex flow modeling across scales

His publication trends indicate a strong focus on merging quantum computing with classical fluid dynamics simulations, developing adaptive solvers like ALPACA, and optimizing numerical schemes for compressible and multiphase flows. Recent works explore dynamic circuits for quantum lattice-Boltzmann methods and multi-objective optimization frameworks.

Dr. Winter's articles cover diverse aspects of fluid mechanics, including:

• Quantum computing applications in fluid simulation
• Level-set methods for interface dynamics
• BAYESIAN OPTIMIZATION OF NUMERICAL SCHEMES
• Multi-fidelity surrogate models for complex flows
• High-order methods for conservation laws
• Deep reinforcement learning for dynamic systems