论文标题
通过卷积，复发性神经网络学到的表面扩散的形态演化：推断和预测不确定性
Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: extrapolation and prediction uncertainty
论文作者
论文摘要
我们使用卷积复发性神经网络方法来学习由表面扩散驱动的形态进化。为此，我们首先使用相位场模拟生产训练集。有意，我们仅在相对简单的孤立形状中插入。经过适当的数据增强，训练和验证后，该模型被证明可以正确预测以前未观察到的形态的演变，并以大小的大小了解了进化时间的正确缩放。重要的是，我们基于自举 - 聚集程序来量化预测不确定性。事实证明，后者在将模型应用于更复杂的初始条件时（例如，导致高方面比例的单个结构分裂）时，指出了高度不确定性的基础。讨论了训练集的自动智能授权和混合模拟方法的设计。
We use a Convolutional Recurrent Neural Network approach to learn morphological evolution driven by surface diffusion. To this aim we first produce a training set using phase field simulations. Intentionally, we insert in such a set only relatively simple, isolated shapes. After proper data augmentation, training and validation, the model is shown to correctly predict also the evolution of previously unobserved morphologies and to have learned the correct scaling of the evolution time with size. Importantly, we quantify prediction uncertainties based on a bootstrap-aggregation procedure. The latter proved to be fundamental in pointing out high uncertainties when applying the model to more complex initial conditions (e.g. leading to splitting of high aspect-ratio individual structures). Automatic smart-augmentation of the training set and design of a hybrid simulation method are discussed.
