The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Bayesian neural networks for uncertainty quantification in data-driven materials modeling
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Understanding predictive information criteria for Bayesian models
Gelman, A., Hwang, J., & Vehtari, A. (2014) · Statistics and Computing · doi:10.1007/s11222-013-9416-2 · Cited by 2,104
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Weight Uncertainty in Neural Networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1505.05424 · Cited by 1,342
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510
Mazloumi, E., Rose, G., Currie, G., & Moridpour, S. (2011) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2010.11.004 · Cited by 133
Cited by
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Psaros, A. F., Meng, X., Zou, Z., Guo, L., & Karniadakis, G. E. (2023) · Journal of Computational Physics · doi:10.1016/j.jcp.2022.111902 · Cited by 455
Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
Yu, J., Wang, D., & Zheng, M. (2022) · iScience · doi:10.1016/j.isci.2022.104814 · Cited by 59
Metadata
- Authors
- Olivier, A., Shields, M. D., & Graham-Brady, L.
- Venue
- Computer Methods in Applied Mechanics and Engineering
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 386
- Pages
- 114079
- ISSN
- 0045-7825 (ISSN-L)
- OpenAlex
- W3195348523
- Cited by
- 191
- References
- 8
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Olivier, A., Shields, M. D., & Graham-Brady, L. (2021). Bayesian neural networks for uncertainty quantification in data-driven materials modeling. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.1016/j.cma.2021.114079
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