34 citations · 41 across the 9 of their papers we have counts for
9 papers
Spatio-Temporal Prediction of Unsteady Airfoil Aerodynamics Using Augmented Graph Neural Ordinary Differential Equations with Exogenous Controls
Henrik Lange, Reik Thormann, Philipp Bekemeyer
Unsteady aerodynamic phenomena, such as gusts, turbulence, and fluid-structure interactions affect an aircraft during flight. For design, optimisation and certification, it is indi…
Evaluation of State-of-the-Art Deep Learning Architectures for Aerodynamical Predictions
Jan Scherz, Derrick Hines, Philipp Bekemeyer
Surrogate models are used to substitute classical numerical solvers in engineering applications where the computational cost of the latter becomes infeasible. For instance, in aero…
Solving Nonlinear Partial Differential Equations via a Hybrid Newton Method Using Quantum Linear System Solver
Maximilian Mandelt Buxadé, Stefan Langer, Philipp Bekemeyer
To approximate solutions of complex nonlinear partial differential equations remains a computational challenge, especially for sets of equations relevant in industry, such as Euler…
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…
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…
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…
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…