DyFraNet：使用2D到3D深度神经网络预测和回溯时空动态断裂力学
DyFraNet: Forecasting and Backcasting Dynamic Fracture Mechanics in Space and Time Using a 2D-to-3D Deep Neural Network
摘要：
材料失效的动力学是一系列科学和工程领域中最关键的现象之一，从医疗保健到结构材料再到运输。在本文中，我们提出了一种专门设计的深度神经网络DyFraNet，它可以通过识别断裂传播的完整历史来预测动态断裂行为-从开裂开始，随着裂纹在材料中的生长，建模为一系列随时间推移而演变的框架，并且相互依赖。此外，该模型不仅可以预测未来的破裂过程，而且还可以通过反演来阐明过去的破裂历史。在这种情况下，一旦提供了断裂事件的结果，模型将阐明导致这种状态的过去事件，并将预测故障过程的未来演变。通过将预测结果与原子级模拟和理论进行比较，我们表明DyFraNet可以通过准确预测裂纹如何随时间发展来捕获动态断裂力学，包括裂纹速度等措施，以及裂纹何时变得不稳定。我们使用GradCAM来解释DyFrNet如何感知几何条件和断裂动力学之间的关系，我们发现DyFrNet特别关注裂纹尖端周围的区域，这些区域在断裂扩展的早期阶段具有关键影响。在后期阶段，模型更加关注材料中现有或新形成的损伤分布。所提出的方法提供了显着的潜力，以加速探索的材料设计的动态断裂失效，并可以有益地适用于各种动态工程问题。
英文摘要：
The dynamics of materials failure is one of the most critical phenomena in a range of scientific and engineering fields, from healthcare to structural materials to transportation. In this paper we propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation - from cracking onset, as a crack grows through the material, modeled as a series of frames evolving over time and dependent on each other. Furthermore, this model can not only forecast future fracture processes but also backcast to elucidate the past fracture history. In this scenario, once provided with the outcome of a fracture event, the model will elucidate past events that led to this state and will predict the future evolution of the failure process. By comparing the predicted results with atomistic-level simulations and theory, we show that DyFraNet can capture dynamic fracture mechanics by accurately predicting how cracks develop over time, including measures such as the crack speed, as well as when cracks become unstable. We use GradCAM to interpret how DyFraNet perceives the relationship between geometric conditions and fracture dynamics and we find DyFraNet pays special attention to the areas around crack tips, which have a critical influence in the early stage of fracture propagation. In later stages, the model pays increased attention to the existing or newly formed damage distribution in the material. The proposed approach offers significant potential to accelerate the exploration of the dynamics in material design against fracture failures and can be beneficially adapted for all kinds of dynamical engineering problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
