MétaCan
Menu
Back to cohort
Record W2100222787 · doi:10.1109/mwsym.2006.249421

TLM-based Self-adjoint Sensitivities of S-parameters with Time-domain Electromagnetic Solvers

2006· article· en· W2100222787 on OpenAlex
Ying Li, Natalia K. Nikolova, Mohamed H. Bakr

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTransmission-line matrix methodSolverDiscretizationFinite-difference time-domain methodSensitivity (control systems)Transmission lineElectromagnetic fieldComputational electromagneticsComputationComputer scienceVoltageTime domainElectromagneticsElectronic engineeringPhysicsMathematicsAlgorithmMathematical analysisElectrical engineeringEngineeringOptics

Abstract

fetched live from OpenAlex

We present a self-adjoint approach to S-parameter sensitivity computation with time-domain electromagnetic (EM) simulators based on the transmission-line matrix (TLM) discretization scheme. The method is applicable with any EM simulator, which can export either the electric field or the incident TLM voltages at user defined points. Our technique converts the electric and magnetic field solution into TLM voltages if the latter are not available (e.g., in FDTD-based simulators). The S-parameter derivatives are computed as an independent post-process whose computational requirements are negligible compared to the full-wave system analysis. Adjoint simulations are not needed if the problem is homogeneous. Our approach is illustrated through waveguide problems solved with a commercial TLM solver

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.182
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it