Solver Recommendation For Transport Problems in Slabs Using Machine Learning

Autor:	Chen, Jinzhao, Patel, Japan K., Vasques, Richard
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Nuclear Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	The use of machine learning algorithms to address classification problems is on the rise in many research areas. The current study is aimed at testing the potential of using such algorithms to auto-select the best solvers for transport problems in uniform slabs. Three solvers are used in this work: Richardson, diffusion synthetic acceleration, and nonlinear diffusion acceleration. Three parameters are manipulated to create different transport problem scenarios. Five machine learning algorithms are applied: linear discriminant analysis, K-nearest neighbors, support vector machine, random forest, and neural networks. We present and analyze the results of these algorithms for the test problems, showing that random forest and K-nearest neighbors are potentially the best suited candidates for this type of classification problem. Comment: Accepted -- The International Conference on Mathematics and Computational Methods applied to Nuclear Science and Engineering (M&C 2019), Portland, OR
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.08259 Zobrazit plný text záznamu View this record from Arxiv