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 …
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 …
[KIRJA][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 …
[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 …
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 …
[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 …
[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 …
A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems
L Wang, Y Zhao, J Liu - Mechanical Systems and Signal Processing, 2023 - Elsevier
When dealing with optimization problems, the introduction of uncertainty will greatly
increase the difficulty of solving the problem. The traditional reliability-based design …
increase the difficulty of solving the problem. The traditional reliability-based design …
LIF: A new Kriging based learning function and its application to structural reliability analysis
Z Sun, J Wang, R Li, C Tong - Reliability Engineering & System Safety, 2017 - Elsevier
The main task of structural reliability analysis is to estimate failure probability of a studied
structure taking randomness of input variables into account. To consider structural behavior …
structure taking randomness of input variables into account. To consider structural behavior …