A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Haribabu, Gowtham Nimmal; Jegadeesan, Jeyapriya Thimukonda; Bhattacharya, Chiranjib; Basu, Bikramjit

doi:10.1016/j.commatsci.2023.112512

Computational Materials Science

2023

DOI: 10.1016/j.commatsci.2023.112512

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A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Gowtham Nimmal Haribabu,

Jeyapriya Thimukonda Jegadeesan,

Chiranjib Bhattacharya

et al.

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2024

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Cited by 2 publications

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Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

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Generative adversarial networks (GANs), as a powerful tool for inverse materials discovery, are being increasingly applied in various fields of materials science. This review provides systematic investigations on the applications of GANs from a group of different aspects. The basic principles of GANs are first introduced; then a detailed review of GANs‐based studies regarding distinct scenarios across composition design, processing optimization, crystal structure search, microstructure characterization and defect detection is presented. At the end, several challenges and possible solutions are discussed and outlined. This overview highlights the efficacy of GANs in materials science, and may stimulate the further use of GANs for more intriguing achievements.

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Applications of generative adversarial networks in materials science

Jiang,

Li,

Yang

et al. 2024

Materials Genome Engineering Advances

View full text Add to dashboard Cite

show abstract

Generation of micrograph-annotation pairs for steel microstructure recognition using the hybrid deep generative model in the case of an extremely small and imbalanced dataset

Shen,

Zhao,

Huang

et al. 2024

Materials Characterization

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A deep adversarial approach for the generation of synthetic titanium alloy microstructures with limited training data

Cited by 2 publications

References 37 publications

Applications of generative adversarial networks in materials science

Applications of generative adversarial networks in materials science

Generation of micrograph-annotation pairs for steel microstructure recognition using the hybrid deep generative model in the case of an extremely small and imbalanced dataset

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