High-order Adaptive Rank Integrators for Multi-scale Linear Kinetic Transport Equations in the Hierarchical Tucker Format
<|reference_start|>High-order Adaptive Rank Integrators for Multi-scale Linear Kinetic Transport Equations in the Hierarchical Tucker Format: In this paper, we present a new adaptive rank approximation technique for computing solutions to the high-dimensional linear kinetic transport equation. The approach we propose is based on a macro-micro decomposition of the kinetic model in which the angular domain is discretized with a tensor product quadrature rule under the discrete ordinates method. To address the challenges associated with the curse of dimensionality, the proposed low-rank method is cast in the framework of the hierarchical Tucker decomposition. The adaptive rank integrators we propose are built upon high-order discretizations for both time and space. In particular, this work considers implicit-explicit discretizations for time and finite-difference weighted-essentially non-oscillatory discretizations for space. The high-order singular value decomposition is used to perform low-rank truncation of the high-dimensional time-dependent distribution function. The methods are applied to several benchmark problems, where we compare the solution quality and measure compression achieved by the adaptive rank methods against their corresponding full-grid methods. We also demonstrate the benefits of high-order discretizations in the proposed low-rank framework.<|reference_end|>
@article{sands2024high-order,
    title={High-order Adaptive Rank Integrators for Multi-scale Linear Kinetic
  Transport Equations in the Hierarchical Tucker Format},
    author={William A. Sands and Wei Guo and Jing-Mei Qiu and Tao Xiong},
    journal={arXiv preprint arXiv:2406.19479},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19479},
    primaryClass={math.NA cs.NA physics.comp-ph}
}