论文标题
能量表面高斯过程建模中的修改噪声内核
Modified noise kernels in Gaussian process modelling of energy surfaces
论文作者
论文摘要
我们探讨了在高斯过程建模中非同质噪声内核的使用，以改善描述随机电子结构数据的势能曲线模型。我们在能量曲线上使用相同的噪声内核来描述确定性电子结构数据，通过创建非基础噪声模型。我们观察到模型的噪声和随机数据之间的一致性，当在确定性数据上使用人工噪声时，曲线的强正正规化，从而改善了过度拟合方案中的高斯过程。
We explore the use of non homogenous noise kernels in Gaussian process modelling to improve the potential energy curve models describing stochastic electronic structure data. We use the same noise kernels on energy curves describing deterministic electronic structure data by creating non-homogenous noise model. We observe, as well as agreement between the noise of the model and the stochastic data, a strong regularisation of the curves when using artificial noises on deterministic data which improves Gaussian processes in the over fitting regime.
