发表机构
Aragon Institute of Engineering Research (I3A). Universidad de Zaragoza（阿拉贡工程研究所（I3A）。萨拉戈萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对传统数字孪生局限提出零样本数字孪生框架，核心是热力学知识图神经网络架构，集成辅助网络并通过闭环数据同化弥合模拟与现实差距，在两种物理状态下测试，能在新几何上精确模拟且满足实时要求并可投影变量。
AI 中文摘要
传统预测性数字孪生在几何上往往很僵化，物理域或边界条件变化时需大量重新训练或微调。为克服此局限，我们提出零样本数字孪生新框架，将实时视觉感知与几何无关、物理知识驱动的推理引擎无缝结合。核心是热力学知识图神经网络架构，通过图消息传递实现能量守恒和非负熵产生。还集成辅助图神经网络从稀疏初始视觉边界推断不可观测场，减轻数值启动瞬态。通过连续闭环数据同化机制弥合模拟与现实差距，实时跟踪宏观变形和自由表面流体边界，动态校正自回归模拟并消除数值漂移。在粘弹性梁大变形和粘性流体非线性晃动两种不同物理状态下测试，该统一框架无需特定案例重新训练就能在新的、未见几何形状上进行物理精确模拟，实时延迟约每帧25毫秒，并能通过增强现实直接投影潜在机械变量。
英文摘要
Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions change. To overcome this limitation, we present a novel framework for \textit{Zero-Shot Digital Twins} that seamlessly couples real-time visual perception with a geometry-agnostic, physics-informed reasoning engine. At the core of our architecture is the Thermodynamics-Informed Graph Neural Network architecture, a Geometric Deep Learning solver grounded in a metriplectic thermodynamic formalism that enforces energy conservation and non-negative entropy production locally through graph message passing. The framework integrates an auxiliary Graph Neural Network to infer unobservable fields (such as stress tensors or velocity and energy distributions) directly from sparse initial visual boundaries, mitigating numerical start-up transients. To bridge the sim-to-real gap, we implement a continuous closed-loop data assimilation mechanism; the pipeline tracks macroscopic deformations and free-surface fluid boundaries in real-time using deep segmentation networks combined with sparse optical flow, dynamically correcting the autoregressive simulation rollout and eliminating numerical drift. To test the validity of our approach, we demonstrate the extreme generalization capabilities of our approach across two disparate physical regimes: the large deformations of a viscoelastic beam and the non-linear sloshing of a viscous fluid. In both scenarios, the unified framework instantiates physically accurate simulations on novel, unseen geometries without case-specific retraining, operating well within real-time latency budgets (approximately 25 ms per frame) and enabling the direct projection of latent mechanical variables via Augmented Reality.
