Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908474876624896 |
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| author | Alavi, Azadeh Jayasinghe, Sanduni Mahmoodian, Mojtaba Mazaheri, Sam Thangarajah, John Setunge, Sujeeva |
| author_facet | Alavi, Azadeh Jayasinghe, Sanduni Mahmoodian, Mojtaba Mazaheri, Sam Thangarajah, John Setunge, Sujeeva |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins Alavi, Azadeh Jayasinghe, Sanduni Mahmoodian, Mojtaba Mazaheri, Sam Thangarajah, John Setunge, Sujeeva Quantum Physics Machine Learning 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. |
| title | Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2508.00029 |