Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields
- DOI
- 10.1016/j.neunet.2026.109309
- Published
- 2026-12
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109309,
title = {Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields},
author = {Yu Liu and Yanfei Chen and Ruihao Liu and Rui Li and Xin Wang and Zengli Peng and Keyang Tan and Qiuhe Chen},
year = {2026},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109309},
url = {https://doi.org/10.1016/j.neunet.2026.109309}
}RIS
TY - JOUR TI - Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields AU - Yu Liu AU - Yanfei Chen AU - Ruihao Liu AU - Rui Li AU - Xin Wang AU - Zengli Peng AU - Keyang Tan AU - Qiuhe Chen PY - 2026 JO - Neural Networks DO - 10.1016/j.neunet.2026.109309 UR - https://doi.org/10.1016/j.neunet.2026.109309 ER -
APA
Liu, Y., Chen, Y., Liu, R., Li, R., Wang, X., Peng, Z., Tan, K., & Chen, Q. (2026). Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109309
Source records
- crossref · retrieved 2026-09-26T01:26:23.780Z