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Record W3158582340 · doi:10.82308/52544

Finite-element time-domain methods for nonlinear dispersive media

2020· article· en· W3158582340 on OpenAlexfundno aff
David S. Abraham

Bibliographic record

VenueeScholarship@McGill (McGill) · 2020
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNonlinear systemFinite element methodTime domainComputer sciencePhysicsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

Today’s telecommunications infrastructure is increasingly reliant upon complex material interactions with the electromagnetic field. For instance, effects such as dispersion, in which a material’s response to an applied field depends on its frequency, and nonlinearity, where the response is a complex function of field strength, form the cornerstones of fields such as nonlinear fibre optics. In consequence, efficient, accurate, and reliable numerical simulation tools capable of modeling these complex interactions are increasingly in demand, as cost effective alternatives to physical experimentation and prototyping.In this thesis, a family of Finite-Element Time-Domain (FETD) based numerical methods for the simulation of electromagnetic problems containing electrically complex material interactions is presented. Making use of both the mixed and vector wave equation formulations, the derived methods are capable of modeling very general combinations of linear dispersion,instantaneous nonlinearity, and dispersive nonlinearity within the nonlinear Maxwell’s Equations, free from any simplifying assumptions about the nature of the field solutions. In contrast to existing methods, these techniques permit increased geometric freedom, improved stability, and are capable of handling arbitrarily high nonlinear and dispersive orders.This thesis also presents several additional tools and methods to increase the effectiveness and versatility of these techniques. For instance, a Perfectly Matched Layer (PML) is derived which is compatible with nonlinear dispersive media, permitting the emulation of infinite domains as well as the truncation of finite systems within the nonlinear FETD method. More specifically, by utilizing the stretched coordinate formulation of the PML, the resulting technique not only saves computational resources, but does so without significantly altering the original underlying algorithms.Furthermore, while the derived techniques permit a much more accurate and general solution to Maxwell’s Equations for complex media, they unfortunately do so with a significant added computational burden. To mitigate this fact, this thesis also presents an analysis and breakdown of the computational overhead and bottlenecks associated with these methods. Building upon this analysis, a scheme is then presented by which these algorithms may be accelerated via parallelism and implemented on Graphics Processing Units (GPUs) to help alleviate some of the burden they pose.Lastly, in each case the FETD, PML, and GPU algorithms proposed in this dissertation are tested via numerical studies to verify their proper functioning, convergence, and accuracy. These include convergence studies as well as the demonstration of several well-known and physically significant nonlinear phenomena, such as spatial solitons, temporal solitons, and supercontinuum generation. Moreover, a parallel GPU implementation of the nonlinear algorithm is benchmarked against an equivalent traditional serial Central Processing Unit (CPU) version, and is shown to perform up to 150 times faster, significantly increasing the applicability and usefulness of these algorithms

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.025
GPT teacher head0.287
Teacher spread0.263 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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