- Published
- 2021-03-12
- Venue
- Computer Methods in Applied Mechanics and Engineering, vol. 379, pp. 113741
- Cited by
- 1,321
- Open access
- No free copy found
- DOI
- 10.1016/j.cma.2021.113741
Cite this paper
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- APA 7
Haghighat, E., Raissi, M., Moure, A., Gómez, 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
- MLA 9
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, pp. 113741, https://doi.org/10.1016/j.cma.2021.113741.
- Chicago
Haghighat, Ehsan, Maziar Raissi, Adrian Moure, Héctor Gómez, 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.
- GOST
Haghighat E., Raissi M., Moure A. [и др.] A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics // Computer Methods in Applied Mechanics and Engineering. — 2021. — Vol. 379. — P. 113741. — URL: https://doi.org/10.1016/j.cma.2021.113741
- BibTeX
@article{haghighat2021physicsinformed, title = {{A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics}}, author = {Ehsan Haghighat and Maziar Raissi and Adrian Moure and Héctor Gómez and Ruben Juanes}, journal = {Computer Methods in Applied Mechanics and Engineering}, year = {2021}, volume = {379}, pages = {113741}, doi = {10.1016/j.cma.2021.113741}, url = {https://doi.org/10.1016/j.cma.2021.113741} }- RIS
TY - JOUR AU - Haghighat, Ehsan AU - Raissi, Maziar AU - Moure, Adrian AU - Gómez, Héctor AU - Juanes, Ruben TI - A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics JO - Computer Methods in Applied Mechanics and Engineering VL - 379 SP - 113741 PY - 2021 DA - 2021/03/12 DO - 10.1016/j.cma.2021.113741 UR - https://doi.org/10.1016/j.cma.2021.113741 ER -