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Domain specific language for finite element modeling and simulation

2024· article· en· W4396570721 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvances in Engineering Software · 2024
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsnot available
FundersU.S. Nuclear Regulatory CommissionCanadian Nuclear Safety CommissionU.S. Department of Energy
KeywordsFinite element methodComputer scienceDomain (mathematical analysis)Domain-specific languageProgramming languageMathematicsEngineeringStructural engineeringMathematical analysis

Abstract

fetched live from OpenAlex

A domain specific language (DSL), called the Finite Element Interpreter ( FEI ), is developed and presented. The FEI DSL is designed and developed specifically for linear and nonlinear finite element analysis of static and dynamic behavior of soils and structures. While the main focus is on soils and structures, developed DSL can be used in many other fields of linear and nonlinear analysis of solids and structures in other fields of engineering. The design of FEI takes advantage of modern computer capabilities and text editing software to develop a language which promotes (i) self-documenting analysis scripts, (ii) promotes understanding of finite element modeling , (iii)awareness to physical units, (iv) provides problem specific language constructs, and (v) is safe to execute. Simple examples are used to describe FEI DSL, while many other, more sophisticated, realistic examples are available through the Real-ESSI web site http://real-essi.info .

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.222
Threshold uncertainty score0.756

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 communication0.0000.001
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 head0.009
GPT teacher head0.262
Teacher spread0.254 · 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