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            <title xml:lang="en">An Attention-Augmented Long Short-Term Memory Network for PINN-based Structural Health Monitoring</title>
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            <idno type="halRefHtml">&lt;i&gt;Engineering Structures&lt;/i&gt;, 2025, 340, pp.120673. &lt;a target="_blank" href="https://dx.doi.org/10.1016/j.engstruct.2025.120673"&gt;&amp;#x27E8;10.1016/j.engstruct.2025.120673&amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">An Attention-Augmented Long Short-Term Memory Network for PINN-based Structural Health Monitoring</title>
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                <term xml:lang="en">Parameter estimation</term>
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              <p>Structural Health Monitoring (SHM) plays a vital role in maintaining the safety and functionality of structural and mechanical elements in different engineering sectors. Conventional model-based SHM techniques often struggle with the intricacies of complex systems due to oversimplified models. In contrast, data-driven, model-free methods provide speed and scalability but often fail to incorporate system physics, which leads to challenges in generalizing. Physics-informed neural networks (PINNs) have emerged as promising solutions by merging data-driven models with physical insights. Nevertheless, the majority of PINN applications in SHM concentrate on estimating dynamic system states, while inverse estimation remains less explored.&lt;p&gt;Most existing inverse methods demand complete state measurements, which are rarely achievable in practical scenarios. This research presents a novel PINN framework-a physics informed attention-augmented Long Short-Term Memory network (Pi-Attn-LSTM)-designed for estimating parameters in structural systems without the need for comprehensive state data, thereby enhancing its feasibility. The model utilizes a temporal attention mechanism in an encoder-decoder setup to adaptively focus on critical features in sequential data, enhancing the accuracy of parameter estimation in dynamic environments. Validation is executed using both numerical simulations of multi-degree-of-freedom systems, including an IASC-ASCE-Benchmark problem, and experimental tests on data from a scaled aluminium frame. Moreover, comparative studies between the proposed Pi-Attn-LSTM and a typical Pi-LSTM model demonstrate the superior robustness and accuracy of Pi-Attn-LSTM. The extensive validation study shows that Pi-Attn-LSTM not only speeds up convergence but also reliably identifies localized structural degradation, highlighting its potential as a viable tool for SHM in real-world scenarios.&lt;/p&gt;</p>
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