PDE-constrained optimization for virtual sensing in structural dynamics: Full-field displacement and force recovery from sparse sensors
Abstract
Virtual sensing -- recovering full-field structural response from sparse sensor measurements -- is a fundamental challenge in structural health monitoring (SHM). Fracture assessment, fatigue evaluation and remaining useful life (RUL) prognosis all take the applied load as their input, and that load frequently acts on surfaces that cannot be instrumented. An improved load estimate is therefore of direct practical value for SHM and for prognostics and health management (PHM). The mode-based approach reconstructs the displacement field accurately from a small number of sensors and has been applied successfully for that purpose. The load inferred from this reconstructed displacement is far less reliable, because the residual displacement error is amplified when it is mapped back through the dynamic stiffness. This study therefore formulates virtual sensing as a PDE-constrained optimization (PDE-CO) problem in which the elastodynamic equation is an equality constraint and the applied load is the optimization variable, so that displacement and load are estimated together rather than in sequence. To manage the computational cost, the framework separates offline finite element model preparation from online reconstruction. The assembled matrices are reused during online optimization, and the forward and gradient evaluations can use GPU-based parallel computation. The numerical comparisons support improved noise robustness of PDE-CO displacement reconstruction relative to the mode-based approach within the tested settings. These results provide a theoretical and computational foundation for the development of virtual sensing for SHM and PHM.
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BibTeX
@misc{kim2026pdeconstrained,
title = {{PDE-constrained optimization for virtual sensing in structural dynamics: Full-field displacement and force recovery from sparse sensors}},
author = {Minjae Kim and Jaehwan Jeong and Seulki Lee and Jaemin Kim},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.29349},
eprint = {2606.29349},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.29349},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - PDE-constrained optimization for virtual sensing in structural dynamics: Full-field displacement and force recovery from sparse sensors
AU - Minjae Kim
AU - Jaehwan Jeong
AU - Seulki Lee
AU - Jaemin Kim
PY - 2026
DA - 2026/09/08
PB - arXiv
DO - 10.48550/arxiv.2606.29349
UR - https://arxiv.org/abs/2606.29349
AB - Virtual sensing -- recovering full-field structural response from sparse sensor measurements -- is a fundamental challenge in structural health monitoring (SHM). Fracture assessment, fatigue evaluation and remaining useful life (RUL) prognosis all take the applied load as their input, and that load frequently acts on surfaces that cannot be instrumented. An improved load estimate is therefore of direct practical value for SHM and for prognostics and health management (PHM). The mode-based approach reconstructs the displacement field accurately from a small number of sensors and has been applied successfully for that purpose. The load inferred from this reconstructed displacement is far less reliable, because the residual displacement error is amplified when it is mapped back through the dynamic stiffness. This study therefore formulates virtual sensing as a PDE-constrained optimization (PDE-CO) problem in which the elastodynamic equation is an equality constraint and the applied load is the optimization variable, so that displacement and load are estimated together rather than in sequence. To manage the computational cost, the framework separates offline finite element model preparation from online reconstruction. The assembled matrices are reused during online optimization, and the forward and gradient evaluations can use GPU-based parallel computation. The numerical comparisons support improved noise robustness of PDE-CO displacement reconstruction relative to the mode-based approach within the tested settings. These results provide a theoretical and computational foundation for the development of virtual sensing for SHM and PHM.
ER -