Articles | Volume 30, issue 21
https://doi.org/10.5194/hess-30-6843-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
09 Nov 2026
Research article |
| 09 Nov 2026
| 09 Nov 2026
A physically integrated GNN surrogate for microbially-mediated kinetic reactive transport: PHYGNET
Cited articles
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Chen, C., Deng, Y., Qian, J., Ma, H., Ma, L., Wu, J., and Wu, H.: Deep learning-based inversion framework for fractured media characterization by assimilating hydraulic tomography and thermal tracer tomography data: Numerical and field study, Eng. Geol., 350, 107998, https://doi.org/10.1016/j.enggeo.2025.107998, 2025.
De Lucia, M. and Kühn, M.: DecTree v1.0 – chemistry speedup in reactive transport simulations: purely data-driven and physics-based surrogates, Geosci. Model Dev., 14, 4713–4730, https://doi.org/10.5194/gmd-14-4713-2021, 2021.
Demirer, E., Coene, E., Iraola, A., Nardi, A., Abarca, E., Idiart, A., de Paola, G., and Rodríguez-Morillas, N.: Improving the performance of reactive transport simulations using artificial neural networks, Transp. Porous Med., 149, 271–297, https://doi.org/10.1007/s11242-022-01856-7, 2023.
