American Psychological Association 6th edition

Yamasaki, S., Yaji, K., & Fujita, K. (2021). Data-driven topology design using a deep generative model. Structural and Multidisciplinary Optimization, 64(3), 1401-1420. https://doi.org/10.1007/s00158-021-02926-y

ISO-690 (author-date, English)

YAMASAKI, Shintaro, YAJI, Kentaro and FUJITA, Kikuo, 2021. Data-driven topology design using a deep generative model. Structural and Multidisciplinary Optimization. 1 September 2021. Vol. 64, no. 3, p. 1401-1420. DOI 10.1007/s00158-021-02926-y.

Modern Language Association 9th edition

Yamasaki, S., K. Yaji, and K. Fujita. “Data-Driven Topology Design Using a Deep Generative Model”. Structural and Multidisciplinary Optimization, vol. 64, no. 3, Sept. 2021, pp. 1401-20, https://doi.org/10.1007/s00158-021-02926-y.

Mohr Siebeck - Recht (Deutsch - Österreich)

Yamasaki, Shintaro/Yaji, Kentaro/Fujita, Kikuo: Data-driven topology design using a deep generative model, Structural and Multidisciplinary Optimization 2021, 1401-1420.

Emerald - Harvard

Yamasaki, S., Yaji, K. and Fujita, K. (2021), “Data-driven topology design using a deep generative model”, Structural and Multidisciplinary Optimization, Vol. 64 No. 3, pp. 1401-1420.

Warning: These citations may not always be 100% accurate.