Discontinuous Galerkin frequency domain forward modelling for the inversion of electric permittivity in the 2D case
Bibliographic record
Abstract
ABSTRACT We have recently developed a discontinuous Galerkin frequency domain modelling algorithm for the solution of the 2D transverse magnetic Maxwell equations. This method is formulated on an unstructured triangular discretization of the computational domain and makes use of a high order polynomial interpolation of the electromagnetic field components within each triangular element. The discontinuous nature of the approximation naturally allows for a local definition of the interpolation order that is, in combination with a possibly non‐conforming local refinement of the mesh, a key ingredient for obtaining a flexible and accurate discretization method. Moreover, heterogeneity of the propagation media is easily dealt with by assuming element‐wise values of the electromagnetic parameters. In this paper, we propose the use of this discontinuous Galerkin frequency domain method as the forward modelling algorithm for solving the inverse problem for the electric permittivity in the 2D case. The inversion process is based on a gradient minimization technique developed by Pratt for seismological applications. Preliminary numerical results are presented for the imaging of a simplified subsurface model with the aim of assessing the performances of the proposed inversion methodology with regards to the number of frequencies, the number of recorded data and the number of sources.
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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) | 0.000 | 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) | 0.001 | 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 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".