标题： 一种人工神经网络方法求解分子化学平衡问题 显示英文标题

标题： An Artificial Neural Network Approach to the Solution of Molecular Chemical Equilibrium

摘要： 提出了一种求解瞬时化学平衡问题的新方法。 由于化学平衡本质上具有局部特性，可以将其视为温度、氢密度和电子密度所构成的三维参数空间到表示各物种数密度的多个一维空间的映射。 我们利用人工神经网络逼近非线性函数的能力，构建了用于快速高效解决典型恒星大气物理条件下化学平衡问题的神经网络。 神经网络方法的优势在于能够提供一个解析函数，该函数可以快速评估。 通过学习集（覆盖整个参数空间）训练网络，直到相对误差低于1%，网络被训练好。 还使用额外的验证集验证了网络未过拟合。 然后将这些网络应用于太阳大气的真实三维对流模拟快照，显示出良好的泛化性能。 显示英文摘要

摘要： A novel approach is presented for the solution of instantaneous chemical equilibrium problems. The chemical equilibrium can be considered, due to its intrinsically local character, as a mapping of the three-dimensional parameter space spanned by the temperature, hydrogen density and electron density into many one-dimensional spaces representing the number density of each species. We take advantage of the ability of artificial neural networks to approximate non-linear functions and construct neural networks for the fast and efficient solution of the chemical equilibrium problem in typical stellar atmosphere physical conditions. The neural network approach has the advantage of providing an analytic function, which can be rapidly evaluated. The networks are trained with a learning set (that covers the entire parameter space) until a relative error below 1% is reached. It has been verified that the networks are not overtrained by using an additional verification set. The networks are then applied to a snapshot of realistic three-dimensional convection simulations of the solar atmosphere showing good generalization properties.