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Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation

Carmine Valentino, Federico Pichi, Francesco Colace, Dajana Conte, Gianluigi Rozza

Abstract

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

Integrating Artificial Intelligence, Physics, and Internet of Things: A Framework for Cultural Heritage Conservation

Abstract

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

Paper Structure

This paper contains 19 sections, 28 equations, 18 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Reduced Order Models and Physics-Informed Neural Networks
  4. Reduced Order Models
  5. Physics-Informed Neural Networks
  6. A comprehensive framework for cultural heritage
  7. Acquisition Layer
  8. Knowledge-Base Layer
  9. Inference Engine Layer
  10. Application Layer
  11. 3D Model Module: processing geometries to provide simulations
  12. Numerical Results
  13. Offline-Online modules with Inverse Problem Submodule
  14. Test Problem 1: Poisson problem on a rock
  15. Test Problem 2: Parabolic problem on a column
  16. ...and 4 more sections

Figures (18)

  • Figure 1: The architecture associated with the proposed framework consisting of four functional layers: the Acquisition Layer, the Knowledge-Base Layer, the Inference Engine Layer, and the Application Layer.
  • Figure 2: Acquisition of the 3D model related to the rock represented in Figure. The 3D Model (a) is acquired and elaborated to provide to the Knowledge-Base Layer the list of collocation and boundary point for PINNs. In addition, the framework (b) elaborates the mesh and (c) prepares the visualization through an XDMF file.
  • Figure 3: Acquisition of the 3D model related to the column.
  • Figure 4: Acquisition of the 3D model related to the temple.
  • Figure 5: Steps of the model elaboration performed by the 3D Model Module.
  • ...and 13 more figures