The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Machine learning strategies for systems with invariance properties
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Citations
Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Cited by
Machine Learning for Fluid Mechanics
Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020) · Annual Review of Fluid Mechanics · doi:10.1146/annurev-fluid-010719-060214 · Cited by 3,063
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
Ling, J., Kurzawski, A., & Templeton, J. (2016) · Journal of Fluid Mechanics · doi:10.1017/jfm.2016.615 · Cited by 1,624
Self-citationExplainable machine learning for scientific insights and discoveries
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020) · IEEE Access · doi:10.1109/access.2020.2976199 · Cited by 1,040
“A related approach for RANS-modeled Reynolds stresses for high-speedflat-plate turbulent boundary layers was presented in [63], which uses a systematic approach with basis tensor invariants proposed by [64].”
MethodSun, L., Gao, H., Pan, S., & Wang, J.-X. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.112732 · Cited by 1,045
Wang, J.-X., Wu, J.-L., & Xiao, H. (2017) · Physical Review Fluids · doi:10.1103/physrevfluids.2.034603 · Cited by 737
Metadata
- Authors
- Ling, J., Jones, R., & Templeton, J.
- Venue
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Published
- 2016-08
- Volume
- 318
- Pages
- 22-35
- ISSN
- 0021-9991 (ISSN-L)
- OpenAlex
- W2345737627
- Cited by
- 386
- References
- 1
- FWCI
- 15.37
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Ling, J., Jones, R., & Templeton, J. (2016). Machine learning strategies for systems with invariance properties. Journal of Computational Physics. https://doi.org/10.1016/j.jcp.2016.05.003
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.
