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A review of the application of machine learning and data mining approaches in continuum materials mechanics
Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …
engineers to accelerate the development of novel materials, processes and techniques. One …
Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering
The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …
nearly every technological aspect of society. Many thousands of published manuscripts …
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Phase-field modeling is an effective but computationally expensive method for capturing the
mesoscale morphological and microstructure evolution in materials. Hence, fast and …
mesoscale morphological and microstructure evolution in materials. Hence, fast and …
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
Data-driven methods are emerging as an important toolset in the studies of multiscale,
multiphysics, materials phenomena. More specifically, data mining and machine learning …
multiphysics, materials phenomena. More specifically, data mining and machine learning …
Overview: Computer vision and machine learning for microstructural characterization and analysis
Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …
connecting materials structure to composition, process history, and properties …
Material structure-property linkages using three-dimensional convolutional neural networks
The core materials knowledge needed in the accelerated design, development, and
deployment of new and improved materials is most accessible when cast in the form of …
deployment of new and improved materials is most accessible when cast in the form of …
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
The phase-field method is a powerful and versatile computational approach for modeling the
evolution of microstructures and associated properties for a wide variety of physical …
evolution of microstructures and associated properties for a wide variety of physical …
Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials
We propose a deep neural network (DNN) as a fast surrogate model for local stress
calculations in inhomogeneous non-linear materials. We show that the DNN predicts the …
calculations in inhomogeneous non-linear materials. We show that the DNN predicts the …
Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space
The phase-field method is a popular modeling technique used to describe the dynamics of
microstructures and their physical properties at the mesoscale. However, because in these …
microstructures and their physical properties at the mesoscale. However, because in these …
[HTML][HTML] A computer vision approach for automated analysis and classification of microstructural image data
The 'bag of visual features' image representation was applied to create generic
microstructural signatures that can be used to automatically find relationships in large and …
microstructural signatures that can be used to automatically find relationships in large and …