A Solution to the Inverse Problem of Coupled Hydrological and Thermal Regimes
Bibliographic record
Abstract
In typical geological settings, the subsurface hydrological and thermal regimes are often closely coupled. A realistic analysis of the coupled systems requires that the two regimes be considered simultaneously. To make optimal use of the often noisy hydrological and thermal data, it is necessary to adopt an inverse formulation. In this paper, we report some results of our Erst stage investigation, using a steady state, 2—D (cross—section) model. A 2—D isoparametric finite element model is used to discretize the problem, and the nodal values of temperature and hydraulic head, as well as the elemental medium thermal conductivities and permeabilities, are treated as parameters. A generalized non—linear stochastic inverse method of Bayesian type is used for parameter estimation, with the a priori information on the parameters described in terms of the first two moments of the appropiate probability distributions. For computational efficiency, a gradient method is used in the parameter estimation procedure, and the gradient matrix (derivatives of the parameterized system with respect to the parameters), needed in the iteration scheme, is formulated analytically at the elemental level. Numerical results show that the non-linearity of the problem, which is effectively determined by the quality of the a priori information, plays an important role in the performance of the method. With a sufficient number of reasonably well distributed data, the parameters can be well resolved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".