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soilFlux  

Physics-Informed Neural Networks for Soil Water Retention Curves
View on CRAN: Click here


Download and install soilFlux package within the R console
Install from CRAN:
install.packages("soilFlux")

Install from Github:
library("remotes")
install_github("cran/soilFlux")

Install by package version:
library("remotes")
install_version("soilFlux", "0.1.5")



Attach the package and use:
library("soilFlux")
Maintained by
Hugo Rodrigues
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-26
Latest Update: 2026-03-26
Description:
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.
How to cite:
Hugo Rodrigues (2026). soilFlux: Physics-Informed Neural Networks for Soil Water Retention Curves. R package version 0.1.5, https://cran.r-project.org/web/packages/soilFlux. Accessed 25 Oct. 2026.
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