4 citations · 4 across the 6 of their papers we have counts for
7 papers
Exceptional-point conjugate symmetry and migration in fluid-loaded elastic waveguides: restructuring the physical dispersion spectrum
Dong Xiao, Zahra Sharif Khodaei, M. H. Aliabadi
The dispersion spectrum of a fluid-loaded elastic waveguide is a non-Hermitian system with eigenmodes on a two-sheeted Riemann manifold. Although exceptional points (EPs) organize…
Homotopy continuation of viscoelastic waveguide dispersion curves: from intra-manifold tracking to inter-manifold transport
Dong Xiao, Zahra Sharif Khodaei, M. H. Aliabadi
Conventional mode tracking operates in the dark: it traces dispersion branches on the non-Hermitian eigenvalue manifold using only local continuity, unaware of the global Riemann-s…
Discrete Solution Operator Learning for Geometry-Dependent PDEs
Jinshuai Bai, Haolin Li, Zahra Sharif Khodaei +3
Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry indu…
Mode veering and symmetry-protected crossings in conservative elastic waveguides: unified perturbation-theoretic interpretation and adaptive tracking
Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi
Accurate mode tracking is essential for elastic waveguide dispersion analysis in ultrasonic nondestructive evaluation and structural health monitoring. Its reliability, however, de…
Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification
Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi
Physics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This…
Robust impact localisation on composite aerostructures using kernel design and Bayesian fusion under environmental and operational uncertainties
Dong Xiao, Zahra Sharif-Khodaei, M. H. Aliabadi
Impact localisation on composite aircraft structures remains a significant challenge due to operational and environmental uncertainties, such as variations in temperature, impact m…
Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics
Haolin Li, Yuyang Miao, Zahra Sharif Khodaei +1
PINN models have demonstrated capabilities in addressing fluid PDE problems, and their potential in solid mechanics is beginning to emerge. This study identifies two key challenges…