11 citations · 42 across the 12 of their papers we have counts for
12 papers
A Characteristic Mapping Method with Source Terms: Applications to Ideal Magnetohydrodynamics
Xi-Yuan Yin, Philipp Krah, Jean-Christophe Nave +1
This work introduces a generalized characteristic mapping method designed to handle non-linear advection with source terms. The semi-Lagrangian approach advances the flow map, inco…
Automated transport separation using the neural shifted proper orthogonal decomposition
Beata Zorawski, Shubhaditya Burela, Philipp Krah +2
This paper presents a neural network-based methodology for the decomposition of transport-dominated fields using the shifted proper orthogonal decomposition (sPOD). Classical sPOD…
Synthesizing impurity clustering in the edge plasma of tokamaks using neural networks
Zetao Lin, Thibault Maurel-Oujia, Benjamin Kadoch +4
This work investigates the behavior of impurities in edge plasma of tokamaks using high-resolution numerical simulations based on Hasegawa--Wakatani equations. Specifically, it foc…
A robust shifted proper orthogonal decomposition: Proximal methods for decomposing flows with multiple transports
Philipp Krah, Arthur Marmin, Beata Zorawski +2
We present a new methodology for decomposing flows with multiple transports that further extends the shifted proper orthogonal decomposition (sPOD). The sPOD tries to approximate t…
A Characteristic Mapping Method for Vlasov-Poisson with Extreme Resolution Properties
Philipp Krah, Xi-Yuan Yin, Julius Bergmann +2
We propose an efficient semi-Lagrangian characteristic mapping method for solving the one+one-dimensional Vlasov-Poisson equations with high precision on a coarse grid. The flow ma…
Model Order Reduction for the 1D Boltzmann-BGK Equation: Identifying Intrinsic Variables Using Neural Networks
Julian Koellermeier, Philipp Krah, Julius Reiss +1
Kinetic equations are crucial for modeling non-equilibrium phenomena, but their computational complexity is a challenge. This paper presents a data-driven approach using reduced or…
Parametric model order reduction for a wildland fire model via the shifted POD based deep learning method
Shubhaditya Burela, Philipp Krah, Julius Reiss
Parametric model order reduction techniques often struggle to accurately represent transport-dominated phenomena due to a slowly decaying Kolmogorov n-width. To address this challe…
Front Transport Reduction for Complex Moving Fronts
Philipp Krah, Steffen Büchholz, Matthias Häringer +1
This work addresses model order reduction for complex moving fronts, which are transported by advection or through a reaction-diffusion process. Such systems are especially challen…
Supervised Learning for Multi Zone Sound Field Reproduction under Harsh Environmental Conditions
Henry Sallandt, Philipp Krah, Mathias Lemke
This manuscript presents an approach for multi zone sound field reproduction using supervised learning. Traditional multi zone sound field reproduction methods assume constant spee…
Wavelet Adaptive Proper Orthogonal Decomposition for Large Scale Flow Data
Philipp Krah, Thomas Engels, Kai Schneider +1
The proper orthogonal decomposition (POD) is a powerful classical tool in fluid mechanics used, for instance, for model reduction and extraction of coherent flow features. However,…
Model Order Reduction of Combustion Processes with Complex Front Dynamics
Philipp Krah, Mario Sroka, Julius Reiss
In this work we present a data driven method, used to improve mode-based model order reduction of transport fields with sharp fronts. We assume that the original flow field $q(\mat…
An open and parallel multiresolution framework using block-based adaptive grids
Mario Sroka, Thomas Engels, Philipp Krah +3
A numerical approach for solving evolutionary partial differential equations in two and three space dimensions on block-based adaptive grids is presented. The numerical discretizat…