arXiv:2607. 05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability.
By Ana Fernandez Navamuel, A. Javier Omella, Diego Zamora-Sanchez, David Pardo
arXiv:2304.06522v3 Announce Type: replace-cross
Abstract: Signal extraction is difficult when the number of variables $N$ is much larger than the number of observations $M$. We address this problem u...
By Yoh-ichi Mototake, Y-h. Taguchi
The paper presents a physics‑enhanced machine learning method that combines a long short‑term memory network with an Extended Kalman Filter to identify fast‑varying natural frequencies and damping ratios of Linear Time‑Varying systems. Using vibration data and a physics‑based model, the approach is validated on synthetic data from a 2‑blade offshore wind turbine, achieving a maximum RMS error of 0.0012 Hz for the first Fore‑Aft mode. The study also demonstrates robustness to incorrect damping assumptions and improves damping ratio estimation compared to covariance‑driven stochastic subspace identification.
By Melisa Bozaci, Alice Cicirello
arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.
By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga
arXiv:2608. 09071v1 Announce Type: cross Abstract: Forward uncertainty propagation in complex physical systems can induce structured covariance across field-valued outputs.
By Yupei Nie, Lei Wang, Jiasen Liu
arXiv:1811. 05336v2 Announce Type: replace-cross Abstract: Inference for factor models is often hampered by the lack of tractable and accurate variance estimates, which can materially distort downstream analyses.
By Xingwei Hu, Caihong Hu, Cheng-Kuang Wu