کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6928784 1449346 2018 43 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An artificial neural network as a troubled-cell indicator
ترجمه فارسی عنوان
یک شبکه عصبی مصنوعی به عنوان نشانگر مشکل سلولی
کلمات کلیدی
قوانین حفاظت، گارکین متزلزل، محدود کردن، نشانگر مشکوک، شبکه های عصبی مصنوعی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
High-resolution schemes for conservation laws need to suitably limit the numerical solution near discontinuities, in order to avoid Gibbs oscillations. The solution quality and the computational cost of such schemes strongly depend on their ability to correctly identify troubled-cells, namely, cells where the solution loses regularity. Motivated by the objective to construct a universal troubled-cell indicator that can be used for general conservation laws, we propose a new approach to detect discontinuities using artificial neural networks (ANNs). In particular, we construct a multilayer perceptron (MLP), which is trained offline using a supervised learning strategy, and thereafter used as a black-box to identify troubled-cells. The proposed MLP indicator can accurately identify smooth extrema and is independent of problem-dependent parameters, which gives it an advantage over traditional limiter-based indicators. Several numerical results are presented to demonstrate the robustness of the MLP indicator in the framework of Runge-Kutta discontinuous Galerkin schemes, and its performance is compared with the minmod limiter and the minmod-based TVB limiter.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computational Physics - Volume 367, 15 August 2018, Pages 166-191
نویسندگان
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