soilFlux: Physics-Informed Neural Networks for Soil Water Retention Curves
Implements a physics-informed one-dimensional convolutional
neural network (CNN1D-PINN) for estimating the complete soil water
retention curve (SWRC) as a continuous function of matric potential,
from soil texture, organic carbon, bulk density, and depth.
The network architecture ensures strict monotonic decrease of
volumetric water content with increasing suction by construction,
through cumulative integration of non-negative slope outputs
(monotone integral architecture). Four physics-based residual
constraints adapted from Norouzi et al. (2025) <doi:10.1029/2024WR038149>
are embedded in the loss function: (S1) linearity at the dry end
(pF in [5, 7.6]); (S2) non-negativity at pF = 6.2;
(S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative
in the saturated plateau region (pF in [-2, -0.3]). Includes tools
for data preparation, model training, dense prediction, performance
metrics, texture classification, and publication-quality visualisation.
| Version: |
0.1.5 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr (≥ 1.1.0), tidyr (≥ 1.3.0), ggplot2 (≥ 3.4.0), purrr (≥ 1.0.0), tibble (≥ 3.2.0), stringr (≥ 1.5.0), reticulate (≥ 1.34.0), tensorflow (≥ 2.14.0), rlang (≥ 1.1.0) |
| Suggests: |
keras3 (≥ 1.0.0), ggtern, readxl (≥ 1.4.0), scales (≥
1.2.0), knitr, rmarkdown, testthat (≥ 3.0.0), withr |
| Published: |
2026-03-26 |
| DOI: |
10.32614/CRAN.package.soilFlux |
| Author: |
Hugo Rodrigues 
[aut, cre] |
| Maintainer: |
Hugo Rodrigues <rodrigues.machado.hugo at gmail.com> |
| BugReports: |
https://github.com/HugoMachadoRodrigues/soilFlux/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/HugoMachadoRodrigues/soilFlux,
https://hugomachadorodrigues.github.io/soilFlux/ |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.8), TensorFlow (>= 2.14), Keras (>=
3.0) |
| Citation: |
soilFlux citation info |
| Materials: |
README |
| CRAN checks: |
soilFlux results |
Documentation:
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