[HTML][HTML] Review of conventional and advanced non-destructive testing techniques for detection and characterization of small-scale defects
Inspection reliability of small-scale defects, targeting dimensions below 100 µm, is crucial for
structural safety of critical components in high-value applications. Early defects are often …
structural safety of critical components in high-value applications. Early defects are often …
Materials data toward machine learning: advances and challenges
Machine learning (ML) is believed to have enabled a paradigm shift in materials research,
and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of …
and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of …
Combining crystal plasticity and phase field model for predicting texture evolution and the influence of nuclei clustering on recrystallization path kinetics in Ti-alloys
A three-dimensional computational framework has been developed combining a crystal
plasticity (CP) and a phase-field (PF) approach that can efficiently simulate static …
plasticity (CP) and a phase-field (PF) approach that can efficiently simulate static …
Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods
Quantifying uncertainty associated with the microstructure variation of a material can be a
computationally daunting task, especially when dealing with advanced constitutive models …
computationally daunting task, especially when dealing with advanced constitutive models …
[HTML][HTML] Confidence intervals of inversely identified material model parameters: A novel two-stage error propagation model based on stereo DIC system uncertainty
Digital image correlation (DIC) is a powerful tool for characterising materials and
determining material model parameters. To assess the reliability of the full-field …
determining material model parameters. To assess the reliability of the full-field …
Uncertainty quantification of metallic microstructures using principal image moments
A Senthilnathan, I Javaheri, H Zhao… - Computational Materials …, 2022 - Elsevier
The present work uses Markov Random Field (MRF) algorithm to construct large-scale and
statistically-equivalent samples from small-scale experimental data of metallic …
statistically-equivalent samples from small-scale experimental data of metallic …
Methods and applications of machine learning in computational design of optoelectronic semiconductors
The development of high-throughput computation and materials databases has laid the
foundation for the emergence of data-driven machine learning methods in recent years …
foundation for the emergence of data-driven machine learning methods in recent years …
Monotonic Gaussian process for physics-constrained machine learning with materials science applications
Physics-constrained machine learning is emerging as an important topic in the field of
machine learning for physics. One of the most significant advantages of incorporating …
machine learning for physics. One of the most significant advantages of incorporating …
Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks
With the increased use of data-driven approaches and machine learning-based methods in
material science, the importance of reliable uncertainty quantification (UQ) of the predicted …
material science, the importance of reliable uncertainty quantification (UQ) of the predicted …
Performance prediction of gas turbine blade with multi-source random factors using active learning-based neural network
Z Qiu, Y Wang, J Li, Y Xie, D Zhang - Applied Thermal Engineering, 2024 - Elsevier
Rapid and accurate performance acquisition of high-temperature gas turbine blades is
fundamental to energy system design, analysis, and evaluation. Data-driven surrogate …
fundamental to energy system design, analysis, and evaluation. Data-driven surrogate …