Nonlinear model updating through a hierarchical Bayesian modeling framework
A new time-domain probabilistic technique based on hierarchical Bayesian modeling (HBM)
framework is proposed for calibration and uncertainty quantification of hysteretic type …
framework is proposed for calibration and uncertainty quantification of hysteretic type …
Learning black-and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data
We propose a machine learning framework for the data-driven discovery of macroscopic
chemotactic Partial Differential Equations (PDEs)—and the closures that lead to them-from …
chemotactic Partial Differential Equations (PDEs)—and the closures that lead to them-from …
Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions
Damage detection of civil engineering structures needs to consider the effect of normal
environmental variations on structural dynamic properties. This study develops a novel …
environmental variations on structural dynamic properties. This study develops a novel …
[HTML][HTML] Regression Machine Learning Models for the Short-Time Prediction of Genetic Algorithm Results in a Vehicle Routing Problem
IK Singgih, ML Singgih - World Electric Vehicle Journal, 2024 - mdpi.com
Machine learning techniques have advanced rapidly, leading to better prediction accuracy
within a short computational time. Such advancement encourages various novel …
within a short computational time. Such advancement encourages various novel …
Contribution of machine learning in continuous improvement processes
I Mjimer, ES Aoula, ELH Achouyab - Journal of Quality in …, 2022 - emerald.com
Purpose The aim of this study is to predict one of the key performance indicators used to
improve continually production systems using machine learning techniques known by the …
improve continually production systems using machine learning techniques known by the …
Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference
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 …
(modeling) error models. We investigate the use of colored noise to capture the mismatch …
Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol
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 …
has made obtaining reliable long-term forecasts of the disease progression difficult. Simple …
Nonlinear sparse Bayesian learning for physics-based models
This paper addresses the issue of overfitting while calibrating unknown parameters of over-
parameterized physics-based models with noisy and incomplete observations. A semi …
parameterized physics-based models with noisy and incomplete observations. A semi …
A physical domain-based substructuring as a framework for dynamic modeling and reanalysis of systems
HA Jensen, VA Araya, AD Muñoz… - Computer Methods in …, 2017 - Elsevier
A comprehensive physical domain-based formulation of reduced-order models based on
dominant and residual normal modes and interface reduction is presented. The dynamic …
dominant and residual normal modes and interface reduction is presented. The dynamic …
A probabilistic model with spike-and-slab regularization for inferential fault detection and isolation of industrial processes
This article develops a Bayesian latent variable model for inferential fault detection and
isolation using a spike-and-slab regularization technique. Different from conventional …
isolation using a spike-and-slab regularization technique. Different from conventional …