计算物理模型自动推荐中的数据扩充和特征选择
Data augmentation and feature selection for automatic model recommendation in computational physics
摘要：
分类算法最近在计算物理学中找到了应用，用于选择适合于物理系统的环境和状态的数值方法或模型。对于这样的分类任务，标记的训练数据来自数值模拟，并且通常对应于在网格上离散化的物理场。三个具有挑战性的困难出现：缺乏训练数据，他们的高维性，以及不适用于常见的数据增强技术的物理数据。本文介绍了两种算法来解决这些问题，一种是通过特征选择进行降维，另一种是数据增强。这些算法与各种各样的分类器相结合，以进行评估。当结合堆叠合奏由六个多层感知器和脊逻辑斯谛回归，他们能够达到90%的准确率对我们的分类问题的非线性结构力学。
英文摘要：
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data come from numerical simulations and generally correspond to physical fields discretized on a mesh. Three challenging difficulties arise: the lack of training data, their high dimensionality, and the non-applicability of common data augmentation techniques to physics data. This article introduces two algorithms to address these issues, one for dimensionality reduction via feature selection, and one for data augmentation. These algorithms are combined with a wide variety of classifiers for their evaluation. When combined with a stacking ensemble made of six multilayer perceptrons and a ridge logistic regression, they enable reaching an accuracy of 90% on our classification problem for nonlinear structural mechanics.
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。
