4 papers
Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling
David Kivarkis, Waleed Mouhali, Sadruddin Benkadda +1
We develop two deep learning surrogate autoregressive models for the prediction of the temporal evolution of two-dimensional ideal magnetohydrodynamic (MHD) Kelvin-Helmholtz instab…
A Hybrid semi-Lagrangian Flow Mapping Approach for Vlasov Systems: Combining Iterative and Compositional Flow Maps
Philipp Krah, Zetao Lin, R. -Paul Wilhelm +4
We propose a hybrid semi-Lagrangian scheme for the Vlasov--Poisson equation that combines the Numerical Flow Iteration (NuFI) method with the Characteristic Mapping Method (CMM). B…
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 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…