GenVoid：基于实验验证的物理信息生成模型的不确定性感知地下材料缺陷学习
GenVoid: Uncertainty-Aware Learning of Subsurface Material Defects with an Experimentally Validated Physics-Informed Generative Model
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中文总结 AI 辅助
GenVoid提出一种物理信息生成模型框架，从表面位移测量中概率性识别内部空洞，考虑不确定性，并通过可观测性度量指导测量策略，实现非侵入性缺陷表征。
中文摘要 AI 辅助
内部空洞是制造结构中普遍存在的缺陷，然而由于其几何形状隐藏，只能通过可访问的测量间接推断，因此对其表征仍然具有挑战性。在此，我们介绍GenVoid，一个基于物理信息的生成模型框架，用于仅从表面位移测量中识别复杂二维和三维固体中的内部空洞。通过将控制力学纳入生成推理框架，GenVoid能够在线弹性、超弹性和塑性材料行为中实现空洞识别，并适应复杂的二维和三维结构几何形状。重要的是，该框架明确考虑了位移测量中的不确定性和噪声，产生内部空洞几何的概率重建，而非单一确定性估计。我们使用高保真合成数据集和从原位力学实验中获得的实验测量位移场来演示该方法，确立了其从真实位移测量中推断隐藏空洞的能力。为了量化此类推理的基本限制，我们进一步引入了一种可观测性度量，该度量表征了在施加载荷集合下边界测量对内部局部刚度扰动的敏感性。该框架提供了缺陷位置、传感器配置和重建保真度之间的直接联系，使得能够系统评估边界测量的数量和空间分布如何控制空洞识别精度。为此，这些结果建立了一种物理信息和不确定性感知的隐藏缺陷非侵入性表征方法，并为固体力学中逆问题的测量策略设计提供了定量基础。
英文摘要
Internal voids are ubiquitous defects in manufactured structures, yet their characterization remains challenging because their geometry is hidden and can only be inferred indirectly from accessible measurements. Here we introduce \textit{GenVoid}, a physics-informed generative model-based framework for identifying internal voids in complex two- and three-dimensional solids from surface displacement measurements alone. By incorporating the governing mechanics into a generative inference framework, \textit{GenVoid} enables void identification across linear elastic, hyperelastic and plastic material behaviours and accommodates complex two- and three-dimensional structural geometries. Importantly, the framework explicitly accounts for uncertainty and noise in displacement measurements, producing probabilistic reconstructions of internal void geometry rather than a single deterministic estimate. We demonstrate the approach using high-fidelity synthetic datasets and experimentally measured displacement fields obtained from in-situ mechanical experiments, establishing its ability to infer hidden voids from realistic displacement measurements. To quantify the fundamental limits of such inference, we further introduce an observability measure that characterizes the sensitivity of boundary measurements to localized stiffness perturbations within the interior under an ensemble of applied loads. This framework provides a direct connection between defect location, sensor configuration and reconstruction fidelity, enabling systematic assessment of how the number and spatial distribution of boundary measurements govern void-identification accuracy. To this end, these results establish a physics-informed and uncertainty-aware approach for non-invasive characterization of hidden defects and provide a quantitative basis for designing measurement strategies for inverse problems in solid mechanics.
发表机构
- Worcester Polytechnic Institute（伍斯特理工学院）
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