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A deep adversarial learning methodology for designing microstructural material systems

Publication ,  Conference
Li, X; Yang, Z; Catherine Brinson, L; Choudhary, A; Agrawal, A; Chen, W
Published in: Proceedings of the ASME Design Engineering Technical Conference
January 1, 2018

In Computational Materials Design (CMD), it is well recognized that identifying key microstructure characteristics is crucial for determining material design variables. However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Duke Scholars

Published In

Proceedings of the ASME Design Engineering Technical Conference

DOI

ISBN

9780791851760

Publication Date

January 1, 2018

Volume

2B-2018
 

Citation

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Li, X., Yang, Z., Catherine Brinson, L., Choudhary, A., Agrawal, A., & Chen, W. (2018). A deep adversarial learning methodology for designing microstructural material systems. In Proceedings of the ASME Design Engineering Technical Conference (Vol. 2B-2018). https://doi.org/10.1115/DETC201885633
Li, X., Z. Yang, L. Catherine Brinson, A. Choudhary, A. Agrawal, and W. Chen. “A deep adversarial learning methodology for designing microstructural material systems.” In Proceedings of the ASME Design Engineering Technical Conference, Vol. 2B-2018, 2018. https://doi.org/10.1115/DETC201885633.
Li X, Yang Z, Catherine Brinson L, Choudhary A, Agrawal A, Chen W. A deep adversarial learning methodology for designing microstructural material systems. In: Proceedings of the ASME Design Engineering Technical Conference. 2018.
Li, X., et al. “A deep adversarial learning methodology for designing microstructural material systems.” Proceedings of the ASME Design Engineering Technical Conference, vol. 2B-2018, 2018. Scopus, doi:10.1115/DETC201885633.
Li X, Yang Z, Catherine Brinson L, Choudhary A, Agrawal A, Chen W. A deep adversarial learning methodology for designing microstructural material systems. Proceedings of the ASME Design Engineering Technical Conference. 2018.

Published In

Proceedings of the ASME Design Engineering Technical Conference

DOI

ISBN

9780791851760

Publication Date

January 1, 2018

Volume

2B-2018