Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fragmentation processes but the application of pure HOSS simulation is computationally expensive. At the same time, machine learning methods, shown tremendous success in several scientific problems, are increasingly being considered promising alternatives to physics-based models in the scientific domains. Thus, our goal in this work is to build a new data-driven methodology to reconstruct the crack fracture accurately in the spatial and temporal fields. We leverage physical constraints to regularize the fracture propagation in the long-term reconstruction. In addition, we introduce perceptual loss and several extra pure machine learning optimization approaches to improve the reconstruction performance of fracture data further. We demonstrate the effectiveness of our proposed method through both extrapolation and interpolation experiments. The results confirm that our proposed method can reconstruct high-fidelity fracture data over space and time in terms of pixel-wise reconstruction error and structural similarity. Visual comparisons also show promising results in long-term

翻译：混合优化软件套件（HOSS）是一种结合了有限-离散元方法（FDEM）的高级模拟方法，能够高保真地模拟断裂与破碎过程，但纯HOSS模拟的计算成本极高。与此同时，机器学习方法在多个科学问题中展现出巨大成功，正日益被视为科学领域中基于物理模型的有前景的替代方案。因此，本工作的目标是构建一种新的数据驱动方法，以在时空域中精确重建裂纹断裂。我们利用物理约束对长期重建中的断裂传播进行正则化。此外，我们引入感知损失及多种额外的纯机器学习优化方法，以进一步提升断裂数据重建性能。通过外推和内插实验，我们验证了所提方法的有效性。结果表明，该方法在像素级重建误差和结构相似性方面，能够高保真地重建空间与时间上的断裂数据。视觉对比也在长期预测中展示了令人满意的结果。