用物理知识增强AI，智能监测文物损毁风险
Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation
- 融合物联网、AI与物理规律建模文物状态
- 结合PINNs与降维方法，高效模拟环境对文物影响
- 支持真实与复杂几何的可复现仿真，适合文物保护研究
文化遗产保护日益依赖技术革新与领域知识的融合，以实现有效监测与预测性维护。本文提出一种新框架，整合物联网（IoT）与人工智能（AI）技术，并引入物理现象知识。框架分为四层功能模块，支持文化资产三维模型分析与基于数据和物理知识的精细化仿真。核心组件为科学机器学习，特别是物理信息神经网络（PINNs），将物理定律嵌入深度学习模型。为提升计算效率，框架集成降阶方法（如本征正交分解，POD），兼容经典有限元（FE）方法。同时包含自动化处理三维数字副本的工具，支持直接用于仿真。主要贡献包括：面向文化遗产仿真的三维模型处理方法；将数据驱动与物理模型结合的PINNs应用；以及将PINNs与降阶方法融合，高效建模受环境与材料参数影响的退化过程。实验阶段采用可复现的开源代码，在复杂与真实几何上测试框架各关键组件效能，可解决正向与逆向问题。
原文摘要 · Abstract (English)
The conservation of cultural heritage increasingly relies on integrating technological innovation with domain expertise to ensure effective monitoring and predictive maintenance. This paper presents a novel framework to support the preservation of cultural assets, combining Internet of Things (IoT) and Artificial Intelligence (AI) technologies, enhanced with the physical knowledge of phenomena. The framework is structured into four functional layers that permit the analysis of 3D models of cultural assets and elaborate simulations based on the knowledge acquired from data and physics. A central component of the proposed framework consists of Scientific Machine Learning, particularly Physics-Informed Neural Networks (PINNs), which incorporate physical laws into deep learning models. To enhance computational efficiency, the framework also integrates Reduced Order Methods (ROMs), specifically Proper Orthogonal Decomposition (POD), and is also compatible with classical Finite Element (FE) methods. Additionally, it includes tools to automatically manage and process 3D digital replicas, enabling their direct use in simulations. The proposed approach offers three main contributions: a methodology for processing 3D models of cultural assets for reliable simulation; the application of PINNs to combine data-driven and physics-based approaches in cultural heritage conservation; and the integration of PINNs with ROMs to efficiently model degradation processes influenced by environmental and material parameters. The reproducible and open-access experimental phase exploits simulated scenarios on complex and real-life geometries to test the efficacy of the proposed framework in each of its key components, allowing the possibility of dealing with both direct and inverse problems. Code availability: https://github.com/valc89/PhysicsInformedCulturalHeritage
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