Efficient frequency domain technique for electromagnetic scattering from arbitrary objects using the Random Auxiliary Sources
Bibliographic record
Abstract
Summary form only given. Electromagnetic scattering from 3D objects of arbitrary boundary condition is presented implying the use of Random Auxiliary Sources (RAS) method. This technique provides a fast electromagnetic solver for arbitrarily shaped objects in the frequency domain. The technique is based on enforcing the boundary conditions by replacing the object by equivalent random electric and/or magnetic sources that are arbitrarily distributed within a controlled pre-specified domain. The proposed equivalent problems involve the use of few randomly distributed current filament for two dimensional problem (2D) and infinitesimal dipole sources of arbitrary orientations and moments for three dimensional problems, apart from the boundary of the object, which values are determined via least square method. Consequently, no need for singularity extraction is required. An acceptable tolerance bound of the boundary condition satisfaction error is insured using iterative procedure. Nevertheless, an optimum choice of procedure parameters is made via statistical analysis to provide the fastest and yet accurate solution. The present solutions provided by the proposed technique promise significant reductions in the execution time and memory requirements better than method of moment solutions based on surface integral equations. The technique is verified by comparing current distribution on spheres with Mie's series solution. Also, significant simulation time reduction is achieved over the commercial integral equation solver of CST-MWS (CST Microwave Studio, Ver. 2012, Framingham, MA, 2012) package using the method of moments (MoM) with direct or iterative solvers. The potential of the proposed technique can be explored by comparing the execution time and the required number of unknowns with CST-MWS package using only single core processing with double precision. These results are shown as an example. The technique will be presented with more examples to further illustrate the simplicity and efficiency of the proposed technique.
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.
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) | 0.001 | 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 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".