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Low-rank tensor product Richardson iteration for radiative transfer in plane-parallel geometry

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arxiv 2403.14229 v2 pith:TZ2PY2KX submitted 2024-03-21 math.NA cs.NA

classification math.NAcs.NA
keywords productequationtensorlow-rankoperatorradiativespacetransfer
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The radiative transfer equation (RTE) has been established as a fundamental tool for the description of energy transport, absorption and scattering in many relevant societal applications, and requires numerical approximations. However, classical numerical algorithms scale unfavorably with respect to the dimensionality of such radiative transfer problems, where solutions depend on physical as well as angular variables. In this paper we address this dimensionality issue by developing a low-rank tensor product framework for the RTE in plane-parallel geometry. We exploit the tensor product nature of the phase space to recover an operator equation where the operator is given by a short sum of Kronecker products. This equation is solved by a preconditioned and rank-controlled Richardson iteration in Hilbert spaces. Using exponential sums approximations we construct a preconditioner that is compatible with the low-rank tensor product framework. The use of suitable preconditioning techniques yields a transformation of the operator equation in Hilbert space into a sequence space with Euclidean inner product, enabling rigorous error and rank control in the Euclidean metric.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Highly Efficient Rank-Adaptive Sweep-based SI-DSA for the Radiative Transfer Equation via Mild Space Augmentation

    math.NA 2026-03 unverdicted novelty 7.0 of 10

    Rank-adaptive sweep-based SI-DSA with mild space augmentation matches full-rank accuracy and outer iterations while cutting memory and runtime for steady-state RTE even at 30–45% effective rank.

  2. Synthetic Acceleration Preconditioners for Parametric Radiative Transfer Equations based on Trajectory-Aware Reduced Order Models

    math.NA 2025-09 conditional novelty 6.0 of 10

    A trajectory-aware reduced-order preconditioner fixes the offline-online residual mismatch in ROMSAD and solves parametric RTE in 2-3 online iterations on a lattice test.

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