Comparison of Electromagnetic Forces Evaluated in Yee-Lattice Finite-Difference Time-Domain and Lattice-Boltzmann Method
Bibliographic record
Abstract
Electrodynamic simulations in the Yee-lattice finite-difference time-domain method (YL-FDTDM) require half-unit-cell and half-time-unit offsets between electric and magnetic field components. To evaluate energy, momentum, and force in the YL-FDTDM, electric and magnetic fields must be colocated in both space and time. Colocation operations can be computationally intensive while introducing implementation complexity and numerical error. As an alternative to the YL-FDTDM, the Hauser and Verhey electrodynamic lattice-Boltzmann method (HV ELBM) is implemented on a single lattice representing electric and magnetic fields at the same location and time. Since colocation is not required on a single lattice, its central differences are simpler to implement and more accurate. Continuous plane-wave scattering of electric and magnetic fields from a nondispersive dielectric sphere is solved using the YL-FDTDM, the HV ELBM, and Mie scattering analysis. The YL-FDTDM and HV ELBM are compared in terms of numerical error and computational time representing the analytical Mie solution for the Einstein and Laub time-averaged force density. The HV ELBM requires less computational time to evaluate a time-averaged force density with half the error of the YL-FDTDM. At a similar absolute error, the HV ELBM evaluates a time-averaged force density in less than 10% of the computational time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".