使用机器学习求解板中的运输问题
Solver Recommendation For Transport Problems in Slabs Using Machine Learning
摘要：
使用机器学习算法来解决分类问题在许多研究领域都在兴起。目前的研究旨在测试使用这种算法自动选择最佳的求解器在均匀板的运输问题的潜力。在这项工作中使用三个求解器：理查森，扩散合成加速，非线性扩散加速。操纵三个参数以创建不同的传输问题场景。应用了五种机器学习算法：线性判别分析、K-最近邻、支持向量机、随机森林和神经网络。我们提出并分析了这些算法的测试问题的结果，表明随机森林和K-最近邻是潜在的最适合的候选人，这种类型的分类问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
