The mimetic multiscale method for Maxwell’s equations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT We have developed a mimetic multiscale method to simulate quasistatic Maxwell’s equations in the frequency domain. This is especially useful for extensive geophysical models that include small-scale features. Applying the concept of multiscale methods, we avoid setting up a large and costly system of equations on the fine mesh where the material parameters are discretized on. Instead, we build and solve a system on a much coarser mesh. For doing that, it is inevitable to interpolate between fine and coarse meshes. The construction of this coarse-to-fine interpolation is done by solving local, frequency-independent optimization problems for the electric field and the magnetic flux on each coarse cell incorporating the fine-mesh features. Hence, the interpolation operators transfer the fine-mesh material properties onto the coarse simulation mesh. To increase the accuracy of the interpolation, we apply oversampling; i.e., the coarse-cell optimization problems are solved on extended local domains. Previous work on multiscale methods for Maxwell’s equations is not capable of keeping the mimetic properties of the discretization. With our method being mimetic, the properties of the continuous differential operators are preserved in their discrete counterparts and thus, the resulting simulations do not contain spurious modes. We determine the effectiveness of our multiscale construction with coarse-mesh simulations for two examples: a vertical borehole and a mine model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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