APA (7th ed.) Citation

Haghighat, E., Raissi, M., Moure, A., Gomez, H., & Juanes, R. (2021). A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics. Computer methods in applied mechanics and engineering, 379, 113741. https://doi.org/10.1016/j.cma.2021.113741

Chicago Style (17th ed.) Citation

Haghighat, Ehsan, Maziar Raissi, Adrian Moure, Hector Gomez, and Ruben Juanes. "A Physics-informed Deep Learning Framework for Inversion and Surrogate Modeling in Solid Mechanics." Computer Methods in Applied Mechanics and Engineering 379 (2021): 113741. https://doi.org/10.1016/j.cma.2021.113741.

MLA (9th ed.) Citation

Haghighat, Ehsan, et al. "A Physics-informed Deep Learning Framework for Inversion and Surrogate Modeling in Solid Mechanics." Computer Methods in Applied Mechanics and Engineering, vol. 379, 2021, p. 113741, https://doi.org/10.1016/j.cma.2021.113741.