A multiscale finite volume method for Maxwell's equations at low frequencies
Bibliographic record
Abstract
Simulating electromagnetic fields in the quasi-static regime by solving Maxwell's equations is a central task in many geophysical applications. In most cases, geophysical targets of interest exhibit complex topography and bathymetry as well as layers and faults. Capturing these effects with a sufficient level of detail is a huge challenge for numerical simulations. Standard techniques require a very fine discretization that can result in an impracticably large linear system to be solved. A remedy is to use locally refined and adaptive meshes, however, the potential coarsening is limited in the presence of highly heterogeneous and anisotropic conductivities. In this paper, we discuss the application of multiscale finite volume (MSFV) methods to Maxwell's equations in frequency domain. Given a partition of the fine mesh into a coarse mesh the idea is to obtain coarse-to-fine interpolation by solving local versions of Maxwell's equations on each coarsened grid cell. By construction, the interpolation accounts for fine scale conductivity changes, yields a natural homogenization, and reduces the fine mesh problem dramatically in size. To improve the accuracy for singular sources, we use an irregular coarsening strategy. We show that using MSFV methods we can simulate electromagnetic fields with reasonable accuracy in a fraction of the time as compared to state-of-the-art solvers for the fine mesh problem, especially when considering parallel platforms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".