The present paper reviews recent activities on inverse analysis strategies in geotechnical
engineering using Kalman filters, nonlinear Kalman filters, and Markov chain Monte Carlo …

To identify the nonlinear behavior of dynamical systems subjected to external excitations
such as earthquakes, explosions, and impacts, a novel method is proposed for dynamical …

This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm
for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free …

Bayesian synergistic metamodeling (BSM), a novel technique for physical information
infused data-driven metamodeling, is proposed. A core challenge for modeling is to …

Abstract Model selection is a challenging problem that is of importance in many branches of
the sciences and engineering, particularly in structural dynamics. By definition, it is intended …

This paper introduced a new method to exploit chaotic, sparse representations of nonlinear
time series data. The methodology of the algorithm included two steps. First, the proposed …

We report a Bayesian framework for concurrent selection of physics-based models and
(modeling) error models. We investigate the use of colored noise to capture the mismatch …

Introduction The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic
has made obtaining reliable long-term forecasts of the disease progression difficult. Simple …

In a lot of dynamic processes, the fractional differential operators not only appear as discrete
fractional, but they also have a continuous nature in some sense. In the article, we consider …

A Bayesian model selection and parameter estimation algorithm is applied to investigate the
influence of nonlinear and unsteady aerodynamic loads on the limit cycle oscillation (LCO) …