From the 1 of 28 linked papers with an AI index.
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A Memory-Efficient Adjoint State Optimization Method Based on Time-Reversible Dynamical Low-Rank Approximation
Lukas Einkemmer, Julian Mangott
The primary challenge of conducting PDE-constrained optimization for high-dimensional problems, such as kinetic equations, is the often prohibitive memory cost. Computing gradients…
A stable multiplicative dynamical low-rank discretization for the linear Boltzmann-BGK equation
Lena Baumann, Lukas Einkemmer, Christian Klingenberg +1
The numerical method of dynamical low-rank approximation (DLRA) has recently been applied to various kinetic equations showing a significant reduction of the computational effort.…
An energy stable and conservative multiplicative dynamical low-rank discretization for the Su-Olson problem
Lena Baumann, Lukas Einkemmer, Christian Klingenberg +1
Computing numerical solutions of the thermal radiative transfer equations on a finely resolved grid can be costly due to high computational and memory requirements. A numerical red…
Domain decomposition dynamical low-rank for multi-dimensional radiative transfer equations
Stefan Brunner, Lukas Einkemmer, Terry Haut
In this paper, we propose a domain decomposition dynamical low-rank method to solve high-dimensional radiative transfer problems and similar kinetic equations. The algorithm uses a…
Efficient SN-like and PN-like Dynamic Low Rank methods for Thermal Radiative Transfer
Terry Haut, John Loffeld, Lukas Einkemmer +3
Dynamic Low Rank (DLR) methods are a promising way to reduce the computational cost and memory footprint of the high-dimensional thermal radiative transfer (TRT) equations. The TRT…
A simple predictor-corrector scheme without order reduction for advection-diffusion-reaction problems
Thi Tam Dang, Lukas Einkemmer, Alexander Ostermann
Treating diffusion and advection/reaction separately is an effective strategy for solving semilinear advection-diffusion-reaction equations. However, such an approach is prone to s…