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A state-of-the-art review on machine learning-based multiscale modeling, simulation, homogenization and design of materials
Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …
computational technology as well as engineering tools in material modeling and material …
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 …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
The generation of multiphase porous electrode microstructures is a critical step in the
optimisation of electrochemical energy storage devices. This work implements a deep …
optimisation of electrochemical energy storage devices. This work implements a deep …
Stochastic microstructure characterization and reconstruction via supervised learning
Microstructure characterization and reconstruction have become indispensable parts of
computational materials science. The main contribution of this paper is to introduce a …
computational materials science. The main contribution of this paper is to introduce a …
Reconstruction of 3D microstructures from 2D images via transfer learning
R Bostanabad - Computer-Aided Design, 2020 - Elsevier
Computational analysis, modeling, and prediction of many phenomena in materials require
a three-dimensional (3D) microstructure sample that embodies the salient features of the …
a three-dimensional (3D) microstructure sample that embodies the salient features of the …
Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis
The digitized format of microstructures, or digital microstructures, plays a crucial role in
modern-day materials research. Unfortunately, the acquisition of digital microstructures …
modern-day materials research. Unfortunately, the acquisition of digital microstructures …
Digital transformation of thermal and cold spray processes with emphasis on machine learning
Thermal spray technologies continuously evolve to meet new challenges arising from
current and future market needs and requirements. This evolution has been well …
current and future market needs and requirements. This evolution has been well …
Advances in materials informatics: a review
Materials informatics (MI) is aimed to accelerate the materials discovery using computational
intelligence and data science. Progress of MI depends on the strength of database and …
intelligence and data science. Progress of MI depends on the strength of database and …
[HTML][HTML] Feature engineering of material structure for AI-based materials knowledge systems
SR Kalidindi - Journal of Applied Physics, 2020 - pubs.aip.org
This tutorial introduces systematically the foundational concepts undergirding the recently
formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) …
formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) …