Machine learning-based methods in structural reliability analysis: A review
Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical
engineering. However, an accurate SRA in most cases deals with complex and costly …
engineering. However, an accurate SRA in most cases deals with complex and costly …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
[BOOK][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation
B Echard, N Gayton, M Lemaire - Structural safety, 2011 - Elsevier
An important challenge in structural reliability is to keep to a minimum the number of calls to
the numerical models. Engineering problems involve more and more complex computer …
the numerical models. Engineering problems involve more and more complex computer …
[HTML][HTML] Adaptive approaches in metamodel-based reliability analysis: A review
The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
[HTML][HTML] Active learning for structural reliability: Survey, general framework and benchmark
Active learning methods have recently surged in the literature due to their ability to solve
complex structural reliability problems within an affordable computational cost. These …
complex structural reliability problems within an affordable computational cost. These …
Recent advances in reliability analysis of aeroengine rotor system: a review
Purpose To provide valuable information for scholars to grasp the current situations,
hotspots and future development trends of reliability analysis area. Design/methodology …
hotspots and future development trends of reliability analysis area. Design/methodology …
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 6.13 …
The Dakota toolkit provides a flexible and extensible interface between simulation codes
and iterative analysis methods. Dakota contains algorithms for optimization with gradient …
and iterative analysis methods. Dakota contains algorithms for optimization with gradient …
[HTML][HTML] Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications
Recent advancements and developments in artificial intelligence (AI) based approaches
have shifted the manufacturing practices towards the fourth industrial revolution, considered …
have shifted the manufacturing practices towards the fourth industrial revolution, considered …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …