用物理先验引导模型注意力，提升裂纹尖端分割的可信度
Trustworthy AI-based crack-tip segmentation using domain-guided explanations
- 结合可解释AI与领域先验，指导模型关注物理相关区域
- 在数字图像相关数据上实现更优泛化与更可信的解释
- 适合材料断裂力学等高可靠性科学场景使用
确保深度学习模型在高风险科学应用中的可信性与鲁棒性仍是核心挑战。本文提出注意力引导训练框架，融合可解释人工智能技术、定量评估与领域特定先验，引导模型注意力。通过在训练中引入对模型解释的领域反馈，显著提升了模型的泛化能力。我们在数字图像相关数据上的裂纹尖端语义分割任务中验证该方法，使模型注意力与物理上有意义的应力场（如Williams解析解描述的）对齐，从而聚焦于物理相关区域，最终实现更好的泛化性能与更忠实的解释。
原文摘要 · Abstract (English)
Ensuring the trustworthiness and robustness of deep learning models remains a fundamental challenge, particularly in high-stakes scientific applications. In this study, we present a framework called attention-guided training that combines explainable artificial intelligence techniques with quantitative evaluation and domain-specific priors to guide model attention. We demonstrate that domain-specific feedback on model explanations during training can enhance the model's generalization capabilities. We validate our approach on the task of semantic crack tip segmentation in digital image correlation data, which is a key application in the fracture mechanical characterization of materials. By aligning model attention with physically meaningful stress fields, such as those described by Williams' analytical solution, attention-guided training ensures that the model focuses on physically relevant regions. This finally leads to improved generalization and more faithful explanations.
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