Finite-Element Simulation in Bioelectromagnetics Without the Need for Modeling and Meshing
Bibliographic record
Abstract
Biological data often takes the form of regular grids of samples, e.g., obtained by magnetic resonance imaging (MRI). When the finite-element method is used to predict the electromagnetic fields in volumes specified in this way, the data is first transformed to a geometric model, and then the model is subdivided into finite elements. These steps are expensive and can be unreliable. An alternative is proposed that avoids both steps. A regular mesh of rectangular finite elements is superimposed on the MRI grid. Each element may straddle boundaries between different tissues, but the basis functions are constructed in such a way that they respect the material interfaces. The new method is applied to the forward problem in electroencephalography. A circular head model and a model derived from real MRI data are analyzed with the new method. Sampled potentials at the surface of the scalp compare well with those obtained using the conventional finite-element method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".