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

TL;DR

This paper introduces a comprehensive framework combining IoT, AI, and physics-based models for the effective monitoring and preservation of cultural heritage assets, utilizing advanced machine learning and simulation techniques.

Contribution

It presents a novel methodology integrating 3D model processing, Physics-Informed Neural Networks, and Reduced Order Methods for cultural heritage conservation.

Findings

Framework effectively simulates degradation processes.

PINNs successfully combine data-driven and physics-based approaches.

Open-source code demonstrates practical applicability.

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