Physics-informed neural network vs finite element method for modeling coupled water and solute flow in unsaturated soils
Why this work is in the frame
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Bibliographic record
Abstract
Accurate modeling of water infiltration and solute transport in unsaturated soils is critical for various applications. These include optimizing irrigation practices to conserve water and minimize environmental impact, as well as predicting the fate of contaminants in soil and groundwater. This study explores the application of the vanilla physics informed neural network (PINN) approach for modeling the coupled system of water flow and solute transport in unsaturated soils. We compare the performance of PINN with the Galerkin finite element method (FEM) to evaluate their effectiveness. Various techniques are implemented to improve the PINN solver, including adaptive activation functions. Numerical tests were carried out to evaluate the efficiency of the PINN solver in comparison to the FEM. The findings reveal that PINN can achieve accuracy comparable to FEM, albeit at a significantly higher computational cost during training, while maintaining fast inference times.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it