In this article, an overview of Bayesian methods for sequential simulation from posterior
distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is …

Based on a stochastic full-range multiscale model, we propose a data-driven approach to
predict the thermal conductivity of CNT reinforced polymeric nano-composites (PNCs) …

In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided
Deep Markov Model (PgDMM). The framework targets the inference of the characteristics …

We present a web-based framework based on the R shiny package with functional back-end
server in machine learning methods. A 4-tiers architecture is programmed to achieve users' …

The intersection of physics and machine learning has given rise to the physics-enhanced
machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the …

This paper presents a novel algorithm that reconstructs structural responses under unknown
inputs and rank-deficient feedthrough matrix conditions. The algorithm eliminates one of the …

We propose coupling a physics-based reduction framework with a suited response
decomposition technique to derive a component-oriented reduction (COR) approach, which …

In this paper, we provide a comprehensive definition and classification of various sources of
uncertainty within the fields of structural dynamics, system identification, and structural …

Fatigue life estimation in ship structures is a very complex problem, because of the
stochasticity in the wave loading experienced by the hull and the inherent variability in the …

In this work, a novel approach is introduced for accelerating the solution of structural
dynamics problems in the presence of localised phenomena, such as cracks. For this …