论文标题
可观察到的机器学习：对原子范围碰撞产品状态分布的反应物
Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions
论文作者
论文摘要
提供了基于机器学习的模型，以从反应物条件的分布中预测原子范围碰撞的分布并进行了定量测试。这些模型基于反应物和产品状态分布的函数，内核和基于网格的表示。尽管所有三种方法都可以从具有R $^2 $> 0.998的显式准经典轨迹模拟中预测最终状态分布，但基于网格的方法表现最好。尽管发现一种基于功能的方法在计算性能方面要好得多两倍，但基于内核和基于网格的方法是预测准确性，可实用性和通用性的优选。基于函数的方法还缺乏一组通用的模型函数。提出了基于网格的方法对非平衡，多温初始状态分布的应用，这是高超音速流中能量分布的常见情况。还讨论了此类模型在直接模拟蒙特卡洛和计算流体动力学模拟中的作用。
Machine learning-based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel- and grid-based representations of the reactant and product state distributions. While all three methods predict final state distributions from explicit quasi-classical trajectory simulations with R$^2$ > 0.998, the grid-based approach performs best. Although a function-based approach is found to be more than two times better in computational performance, the kernel- and grid-based approaches are preferred in terms of prediction accuracy, practicability and generality. The function-based approach also suffers from lacking a general set of model functions. Applications of the grid-based approach to nonequilibrium, multi-temperature initial state distributions are presented, a situation common to energy distributions in hypersonic flows. The role of such models in Direct Simulation Monte Carlo and computational fluid dynamics simulations is also discussed.
