The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
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Cites
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
Self-citationDropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471
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
Riquelme, C., Tucker, G., & Snoek, J. (2018) · arXiv · doi:10.48550/arxiv.1802.09127 · Cited by 72
Cited by
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
Self-citationPhysics-informed neural networks (PINNs) for fluid mechanics: a review
Cai, S., Mao, Z., Wang, Z., Yin, M., & Karniadakis, G. E. (2021) · Acta Mechanica Sinica · doi:10.1007/s10409-021-01148-1 · Cited by 2,084
Self-citationPhysics-Informed Neural Networks for Heat Transfer Problems
Cai, S., Wang, Z., Wang, S., Perdikaris, P., & Karniadakis, G. E. (2021) · Journal of Heat Transfer · doi:10.1115/1.4050542 · Cited by 1,312
Self-citationMishra, S., & Molinaro, R. (2022) · IMA Journal of Numerical Analysis · doi:10.1093/imanum/drab032 · Cited by 336
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
Farea, A., Yli-Harja, O., & Emmert-Streib, F. (2024) · AI · doi:10.3390/ai5030074 · Cited by 259
Metadata
- Authors
- Yang, L., Meng, X., & Karniadakis, G. E.
- Venue
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 425
- Pages
- 109913
- ISSN
- 0021-9991 (ISSN-L)
- OpenAlex
- W3010849941
- Cited by
- 1,169
- References
- 5
- FWCI
- 41.54
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Yang, L., Meng, X., & Karniadakis, G. E. (2021). B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. Journal of Computational Physics. https://doi.org/10.1016/j.jcp.2020.109913
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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