Publications (9)
Physics-Informed Neural Networks for Transonic Flows around an Airfoil
Simon Wassing, Stefan Langer, Philipp Bekemeyer
Physics-informed neural networks have gained popularity as a deep-learning based parametric partial differential equation solver. Especially for engineering applications, this appr…
Physics-Informed Neural Networks for Parametric Compressible Euler Equations
Simon Wassing, Stefan Langer, Philipp Bekemeyer
The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and eng…
Fusing CFD and measurement data using transfer learning
Alexander Barklage, Philipp Bekemeyer
Aerodynamic analysis during aircraft design usually involves methods of varying accuracy and spatial resolution, which all have their advantages and disadvantages. It is therefore…
Predicting Onflow Parameters Using Transfer Learning for Domain and Task Adaptation
Emre Yilmaz, Philipp Bekemeyer
Determining onflow parameters is crucial from the perspectives of wind tunnel testing and regular flight and wind turbine operations. These parameters have traditionally been predi…
Goal-Driven Adaptive Sampling Strategies for Machine Learning Models Predicting Fields
Jigar Parekh, Philipp Bekemeyer
Machine learning models are widely regarded as a way forward to tackle multi-query challenges that arise once expensive black-box simulations such as computational fluid dynamics a…
Partitioned Surrogates and Thompson Sampling for Multidisciplinary Bayesian Optimization
Susanna Baars, Jigar Parekh, Ihar Antonau +2
The long runtime associated with simulating multidisciplinary systems challenges the use of Bayesian optimization for multidisciplinary design optimization (MDO). This is particula…