The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods
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Citations
Cites
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
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
The future of distributed models: model calibration and uncertainty prediction
Beven, K., & Binley, A. (1992) · Hydrological Processes · doi:10.1002/hyp.3360060305 · Cited by 4,527
e-Learning, online learning, and distance learning environments: Are they the same?
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011) · The Internet and Higher Education · doi:10.1016/j.iheduc.2010.10.001 · Cited by 2,042
Machine learning methods for solar radiation forecasting: A review
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017) · Renewable Energy · doi:10.1016/j.renene.2016.12.095 · Cited by 1,836
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752
Deep Learning on Graphs: A Survey
Zhang, Z., Cui, P., & Zhu, W. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981333 · Cited by 1,633
IEEE Transactions on Neural Networks and Learning Systems
(2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2264575 · Cited by 1,573
Machine learning in manufacturing: Advantages, challenges, and applications
Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319
Metadata
- Authors
- Wang, H., Li, B., Gong, J., & Xuan, F.-Z.
- Venue
- Engineering Fracture Mechanics
- Publisher
- Elsevier BV
- Published
- 2023-05
- Volume
- 284
- Pages
- 109242
- ISSN
- 0013-7944 (ISSN-L)
- OpenAlex
- W4361291832
- Cited by
- 281
- References
- 125
- FWCI
- 31.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods. https://app.cochat.ai/papers/10.1016/j.engfracmech.2023.109242
- The paper
- Wang, H., Li, B., Gong, J., & Xuan, F.-Z. (2023). Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods. Engineering Fracture Mechanics. https://doi.org/10.1016/j.engfracmech.2023.109242
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
