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Neural algorithm for solving differential equations
Open accessAbstract
Finite difference equations are considered to solve differential equations numerically by utilizing minimization algorithms. Neural minimization algorithms for solving the finite difference equations are presented. Results of numerical simulation are described to demonstrate the method. Methods of implementing the algorithms are discussed. General features of the neural algorithms are discussed.
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Cited by
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Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Weak adversarial networks for high-dimensional partial differential equations
Zang, Y., Bao, G., Ye, X., & Zhou, H. (2020) · Journal of Computational Physics · doi:10.1016/j.jcp.2020.109409 · Cited by 444
Piscopo, M. L., Spannowsky, M., & Waite, P. (2019) · Physical Review D · doi:10.1103/physrevd.100.016002 · Cited by 117
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Metadata
- Authors
- Lee, H., & Kang, I. S.
- Venue
- Journal of Computational Physics
- Publisher
- Elsevier BV
- Published
- 1990-11
- Volume
- 91
- Issue
- 1
- Pages
- 110-131
- ISSN
- 0021-9991 (ISSN-L)
- OpenAlex
- W1978075626
- Cited by
- 436
- References
- 0
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
