• Title/Summary/Keyword: Algebraic multigrid methods

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An algebraic multigrids based prediction of a numerical solution of Poisson-Boltzmann equation for a generation of deep learning samples (딥러닝 샘플 생성을 위한 포아즌-볼츠만 방정식의 대수적 멀티그리드를 사용한 수치 예측)

  • Shin, Kwang-Seong;Jo, Gwanghyun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.2
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    • pp.181-186
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    • 2022
  • Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

Multilevel acceleration of scattering-source iterations with application to electron transport

  • Drumm, Clif;Fan, Wesley
    • Nuclear Engineering and Technology
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    • v.49 no.6
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    • pp.1114-1124
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    • 2017
  • Acceleration/preconditioning strategies available in the SCEPTRE radiation transport code are described. A flexible transport synthetic acceleration (TSA) algorithm that uses a low-order discrete-ordinates ($S_N$) or spherical-harmonics ($P_N$) solve to accelerate convergence of a high-order $S_N$ source-iteration (SI) solve is described. Convergence of the low-order solves can be further accelerated by applying off-the-shelf incomplete-factorization or algebraic-multigrid methods. Also available is an algorithm that uses a generalized minimum residual (GMRES) iterative method rather than SI for convergence, using a parallel sweep-based solver to build up a Krylov subspace. TSA has been applied as a preconditioner to accelerate the convergence of the GMRES iterations. The methods are applied to several problems involving electron transport and problems with artificial cross sections with large scattering ratios. These methods were compared and evaluated by considering material discontinuities and scattering anisotropy. Observed accelerations obtained are highly problem dependent, but speedup factors around 10 have been observed in typical applications.