SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- DOI
- 10.1016/j.cma.2020.113552
- Published
- 2021-01
- Container
- Computer Methods in Applied Mechanics and Engineering
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.cma.2020.113552,
title = {SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks},
author = {Ehsan Haghighat and Ruben Juanes},
year = {2021},
journal = {Computer Methods in Applied Mechanics and Engineering},
doi = {10.1016/j.cma.2020.113552},
url = {https://doi.org/10.1016/j.cma.2020.113552}
}RIS
TY - JOUR TI - SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks AU - Ehsan Haghighat AU - Ruben Juanes PY - 2021 JO - Computer Methods in Applied Mechanics and Engineering DO - 10.1016/j.cma.2020.113552 UR - https://doi.org/10.1016/j.cma.2020.113552 ER -
APA
Haghighat, E., & Juanes, R. (2021). SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.1016/j.cma.2020.113552
Source records
- crossref · retrieved 2026-11-10T09:31:52.123Z