Physics-guided machine learning (ML) is an emerging paradigm that combines both data-
driven ML models and physics-based models together to fully take advantage of the data …

Uncertainty quantification (UQ) of dynamical systems with a surrogate model is difficult as
the surrogate model parameters often need to be computed at each time step. For that …

Uncertainty propagation of frequency response functions (FRFs) under parameter variations
is crucial for structural design and reliability analysis. However, obtaining sufficiently large …

In ultrasonic testing field, the design and optimization of point-focusing shear vertical waves
electromagnetic acoustic transducer (PFSV-EMAT) with high energy conversion efficiency …

Uncertainty propagation (UP) of the frequency response is essential for the robust design of
viscoelastic damping structures. One challenge in solving this problem is enormous …

Modeling, analysis and optimizing the stealthiness of the submarine platforms are in the
focus of many research studies. Almost all of these methods have been applied to …

Modal metamodels can be effectively used as surrogates for expensive simulations in
structural dynamics analysis with parametric variations. However, in the case of a system …

Be it due to time constraints or insufficient processing power–or a combination of both–the
use of models with large numbers of Degrees of Freedom (DoF) may be unsuitable to …