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 …
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques
Building sensible processing-structure-property (PSP) links to gain fundamental insights and
understanding of materials behavior has been the focus of many works in computational …
understanding of materials behavior has been the focus of many works in computational …
Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods
D Montes de Oca Zapiain, JA Stewart… - npj Computational …, 2021 - nature.com
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 …
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 …
Bi-directional evolutionary structural optimization on advanced structures and materials: a comprehensive review
The evolutionary structural optimization (ESO) method developed by Xie and Steven
(Comput Struct 49 (5): 885–896, 162), an important branch of topology optimization, has …
(Comput Struct 49 (5): 885–896, 162), an important branch of topology optimization, has …
Microstructural materials design via deep adversarial learning methodology
Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …
Spherical nanoindentation stress–strain curves
S Pathak, SR Kalidindi - Materials science and engineering: R: Reports, 2015 - Elsevier
Although indentation experiments have long been used to measure the hardness and
Young's modulus, the utility of this technique in analyzing the complete elastic–plastic …
Young's modulus, the utility of this technique in analyzing the complete elastic–plastic …
Key computational modeling issues in integrated computational materials engineering
Designing materials for targeted performance requirements as required in Integrated
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Perspective: Machine learning in experimental solid mechanics
Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …
are rapidly proliferating into the discovery process due to significant advances in data …
A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer …
In this paper, a data-driven-based computational homogenization method based on neural
networks is proposed to describe the nonlinear electric conduction in random graphene …
networks is proposed to describe the nonlinear electric conduction in random graphene …