Physics-informed temporal convolutional network with auxiliary process-state trajectories for effluent quality prediction
- DOI
- 10.1016/j.watres.2026.127262
- Published
- 2027-01
- Container
- Water Research
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.127262,
title = {Physics-informed temporal convolutional network with auxiliary process-state trajectories for effluent quality prediction},
author = {Yiting Li and Zongrui Huang and Xiaolin Zhang and Hongpeng Zhang and Zhigang Li and Peng Zhang and Yingjie Xia},
year = {2027},
journal = {Water Research},
doi = {10.1016/j.watres.2026.127262},
url = {https://doi.org/10.1016/j.watres.2026.127262}
}RIS
TY - JOUR TI - Physics-informed temporal convolutional network with auxiliary process-state trajectories for effluent quality prediction AU - Yiting Li AU - Zongrui Huang AU - Xiaolin Zhang AU - Hongpeng Zhang AU - Zhigang Li AU - Peng Zhang AU - Yingjie Xia PY - 2027 JO - Water Research DO - 10.1016/j.watres.2026.127262 UR - https://doi.org/10.1016/j.watres.2026.127262 ER -
APA
Li, Y., Huang, Z., Zhang, X., Zhang, H., Li, Z., Zhang, P., & Xia, Y. (2027). Physics-informed temporal convolutional network with auxiliary process-state trajectories for effluent quality prediction. Water Research. https://doi.org/10.1016/j.watres.2026.127262
Source records
- crossref · retrieved 2026-11-08T11:18:56.559Z