用神经网络从数字库学习材料微结构演化，事件驱动模型更高效
Neural Network Modeling of Microstructure Complexity Using Digital Libraries
- 采用数字库数据训练神经网络预测裂纹扩展与图灵模式
- 脉冲神经元模型精度更高、参数更少、内存占用更低
- 适合研究相变与界面演化的复杂系统，尤其擅长事件驱动建模
物质微结构演化通常通过场或水平集求解器进行数值模拟，其空间时间复杂性可表现为像素/体素数据与矢量图形的双重表示。受此类比启发，结合人工神经网络的结构特性和脉冲神经网络的事件驱动特性，我们评估其在学习和预测疲劳裂纹生长及图灵模式发展中的表现。预测基于由计算机模拟构建的数字库，该库可被实验数据替代，从而突破物理方程的数学强约束。结果表明，漏电积分-放电神经元模型在参数更少、内存消耗更低的情况下，仍具备更高的预测精度，有效缓解了传统计算机视觉任务中的精度-成本权衡。对网络结构的分析显示，这一优势源于其更小的权重范围和更稀疏的连接。研究凸显了事件驱动模型在数字库方法下处理具有演化特征的块体相与界面行为的能力。
原文摘要 · Abstract (English)
Microstructure evolution in matter is often modeled numerically using field or level-set solvers, mirroring the dual representation of spatiotemporal complexity in terms of pixel or voxel data, and geometrical forms in vector graphics. Motivated by this analog, as well as the structural and event-driven nature of artificial and spiking neural networks, respectively, we evaluate their performance in learning and predicting fatigue crack growth and Turing pattern development. Predictions are made based on digital libraries constructed from computer simulations, which can be replaced by experimental data to lift the mathematical overconstraints of physics. Our assessment suggests that the leaky integrate-and-fire neuron model offers superior predictive accuracy with fewer parameters and less memory usage, alleviating the accuracy-cost tradeoff in contrast to the common practices in computer vision tasks. Examination of network architectures shows that these benefits arise from its reduced weight range and sparser connections. The study highlights the capability of event-driven models in tackling problems with evolutionary bulk-phase and interface behaviors using the digital library approach.
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