We present an integrated approach for the use of simulated data from full order discretization as well as projection-based Reduced Basis reduced order models for the training of machine learning approaches, in particular Kernel Methods, in order to achieve fast, reliable predictive models for the chemical conversion rate in reactive flows with varying transport regimes.

翻译：我们提出了一个综合方法,用于使用来自全顺序离散的模拟数据以及基于预测的用于培训机器学习方法,特别是内核方法的减少基本订单模型,以便在具有不同运输系统的被动流动中实现快速、可靠的化学转换率预测模型。