The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: A case study of biochar adsorption.
- DOI
- 10.1016/j.watres.2026.127101
- Published
- 2026-10-13
- Container
- Water Res
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.127101,
title = {The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: A case study of biochar adsorption.},
author = {Fang Y and Wen Y and Zhang Y and Zhang S and You S and Li W.},
year = {2026},
journal = {Water Res},
doi = {10.1016/j.watres.2026.127101},
url = {https://doi.org/10.1016/j.watres.2026.127101}
}RIS
TY - JOUR TI - The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: A case study of biochar adsorption. AU - Fang Y AU - Wen Y AU - Zhang Y AU - Zhang S AU - You S AU - Li W. PY - 2026 JO - Water Res DO - 10.1016/j.watres.2026.127101 UR - https://doi.org/10.1016/j.watres.2026.127101 ER -
APA
Y, F., Y, W., Y, Z., S, Z., S, Y., & W., L. (2026). The necessity of physics-informed neural networks for adsorption-based water treatment machine learning models: A case study of biochar adsorption.. Water Res. https://doi.org/10.1016/j.watres.2026.127101
Source records
- europe-pmc · retrieved 2026-11-10T12:12:07.348Z