NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems. (arXiv:2208.12771v1 [cs.LG])

Structural monitoring for complex built environments often suffers from
mismatch between design, laboratory testing, and actual built parameters.
Additionally, real-world structural identification problems encounter many
challenges. For example, the lack of accurate baseline models, high
dimensionality, and complex multivariate partial differential equations (PDEs)
pose significant difficulties in training and learning conventional data-driven
algorithms. This paper explores a new framework, dubbed NeuralSI, for
structural identification by augmenting PDEs that govern structural dynamics
with neural networks. Our approach seeks to estimate nonlinear …

arxiv identification lg systems