Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative …
Direct prediction of material properties from microstructures through statistical models has
shown to be a potential approach to accelerating computational material design with large …
shown to be a potential approach to accelerating computational material design with large …
Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics
Multiscale simulations are demanding in terms of computational resources. In the context of
continuum micromechanics, the multiscale problem arises from the need of inferring …
continuum micromechanics, the multiscale problem arises from the need of inferring …
Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine-and deep-learning methods
C Herriott, AD Spear - Computational Materials Science, 2020 - Elsevier
In this work, we investigate the performance of data-driven modeling for mechanical property
prediction of a simulated microstructural dataset. The dataset comprises realistic …
prediction of a simulated microstructural dataset. The dataset comprises realistic …
Microstructure representation and reconstruction of heterogeneous materials via deep belief network for computational material design
Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of
complex material systems by integrating material science and design automation. For …
complex material systems by integrating material science and design automation. For …
A transfer learning approach for microstructure reconstruction and structure-property predictions
Stochastic microstructure reconstruction has become an indispensable part of computational
materials science, but ongoing developments are specific to particular material systems. In …
materials science, but ongoing developments are specific to particular material systems. In …
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 …
Stochastic characterization and reconstruction of material microstructures for establishment of process-structure-property linkage using the deep generative model
S Noguchi, J Inoue - Physical Review E, 2021 - APS
In material design, microstructure characterization and reconstruction are indispensable for
understanding the role of a structure in a process-structure-property relation. The significant …
understanding the role of a structure in a process-structure-property relation. The significant …
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
Data-driven methods are attracting growing attention in the field of materials science. In
particular, it is now becoming clear that machine learning approaches offer a unique avenue …
particular, it is now becoming clear that machine learning approaches offer a unique avenue …
A deep adversarial learning methodology for designing microstructural material systems
In Computational Materials Design (CMD), it is well recognized that identifying key
microstructure characteristics is crucial for determining material design variables. However …
microstructure characteristics is crucial for determining material design variables. However …
Microstructure design using machine learning generated low dimensional and continuous design space
The cornerstone of materials design is the design space used in solving materials related
optimization problems. Materials design strategies often involve evaluating properties of …
optimization problems. Materials design strategies often involve evaluating properties of …