Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network
Emulating microstructural evolution during spinodal decomposition using a tensor decomposed convolutional and recurrent neural network
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DOI：
10.1016/j.commatsci.2023.112187
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发表时间：
2023-05
影响因子：
3.3
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中科院分区：
文献类型：
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相场模型是模拟金属材料、聚合物和陶瓷微结构演化的最有力的工具之一。然而，现有的PF方法依赖于严格的数学模型开发、复杂的数值格式和高性能的计算来保证精度。虽然最近发展的替代微结构模型使用了深度学习技术和从低维数据重建微结构，但其精度相当有限，因为在追求降维的过程中会丢失时空信息。鉴于这些局限性，我们提出了一种用于预测微结构演化的新型数据驱动仿真器(DDE)，该仿真器将基于图像的卷积和递归神经网络(CRNN)与张量分解相结合，同时利用先前获得的PF数据集进行训练。为了评估DDE的稳健性，我们还对几个噪声初始状态的仿真序列和标度行为与相场模拟进行了比较。最后，我们讨论了我们的微结构仿真技术在运行时加速环境中的有效性，同时也强调了它与准确性的权衡。
Phase-field (PF) models are one of the most powerful tools to simulate microstructural evolution in metallic materials, polymers, and ceramics. However, existing PF approaches rely on rigorous mathematical model development, sophisticated numerical schemes, and high-performance computing for accuracy. Although recently developed surrogate microstructure models employ deep-learning techniques and reconstruction of microstructures from lower-dimensional data, their accuracy is fairly limited as spatiotemporal information is lost in the pursuit of dimensional reduction. Given these limitations, we present a novel data-driven emulator (DDE) for predicting microstructural evolution, which combines an image-based convolutional and recurrent neural network (CRNN) with tensor decomposition, while leveraging previously obtained PF datasets for training. To assess the robustness of DDE, we also compare the emulation sequence and the scaling behavior with phase-field simulations for several noisy initial states. Finally, we discuss the effectiveness of our microstructure emulation technique in the context of runtime speed-up while also highlighting its trade-off with accuracy.