Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins

2025-07-30

AI Summary

For real-time digital twin updating in structural health monitoring of large civil infrastructure (e.g., bridges, pipelines, offshore platforms), the high computational cost of inverse finite element inversion poses a critical bottleneck. To address this, this paper proposes a quantum-classical hybrid surrogate model. The method innovatively employs symmetric positive definite (SPD) matrix encoding and polynomial quantum feature embedding, integrating parameterized quantum circuits with a classical multilayer perceptron (QMLP) into an end-to-end inverse mapping architecture for efficient full-field displacement and stress reconstruction from sparse sensor measurements. Evaluated on real-world bridge monitoring data, the model achieves a mean squared error of 3.16×10⁻¹¹—significantly outperforming purely classical approaches—demonstrating its effectiveness and feasibility for real-time, high-fidelity structural state reconstruction.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Large-scale civil structures, such as bridges, pipelines, and offshore platforms, are vital to modern infrastructure, where unexpected failures can cause significant economic and safety repercussions. Although finite element (FE) modeling is widely used for real-time structural health monitoring (SHM), its high computational cost and the complexity of inverse FE analysis, where low dimensional sensor data must map onto high-dimensional displacement or stress fields pose ongoing challenges. Here, we propose a hybrid quantum classical multilayer perceptron (QMLP) framework to tackle these issues and facilitate swift updates to digital twins across a range of structural applications. Our approach embeds sensor data using symmetric positive definite (SPD) matrices and polynomial features, yielding a representation well suited to quantum processing. A parameterized quantum circuit (PQC) transforms these features, and the resultant quantum outputs feed into a classical neural network for final inference. By fusing quantum capabilities with classical modeling, the QMLP handles large scale inverse FE mapping while preserving computational viability. Through extensive experiments on a bridge, we demonstrate that the QMLP achieves a mean squared error (MSE) of 0.0000000000316, outperforming purely classical baselines with a large margin. These findings confirm the potential of quantum-enhanced methods for real time SHM, establishing a pathway toward more efficient, scalable digital twins that can robustly monitor and diagnose structural integrity in near real time.

Problem

• High computational cost of inverse FE analysis
• Mapping low-dimensional sensor data to high-dimensional fields
• Real-time structural health monitoring challenges

Innovation

• Hybrid quantum classical multilayer perceptron framework
• Parameterized quantum circuit transforms features
• Quantum outputs feed classical neural network