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Record W4407560818 · doi:10.4171/owr/2024/35

Polynomial Optimization for Nonlinear Dynamics: Theory, Algorithms and Applications

2025· article· en· W4407560818 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.

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

Bibliographic record

VenueOberwolfach Reports · 2025
Typearticle
Languageen
FieldComputer Science
TopicPolynomial and algebraic computation
Canadian institutionsUniversity of Victoria
FundersAgence Nationale de la RechercheNational Science Foundation
KeywordsNonlinear systemPolynomialPartial differential equationDynamical systems theoryOrdinary differential equationComputer scienceMathematicsApplied mathematicsAlgebraic numberAlgebra over a fieldMathematical optimizationDifferential equationMathematical analysisPure mathematics

Abstract

fetched live from OpenAlex

This workshop focused on using computational tools of polynomial optimization to deduce information about nonlinear dynamical systems, including systems governed by ordinary or partial differential equations. This approach sits at the interface of various research areas, requiring combinations of applied nonlinear dynamics and control theory, polynomial optimization, real algebraic geometry, partial differential equations, and variational analysis. The workshop brought together researchers in these different areas to share recent advances and to build the connections required for further progress.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.946
Threshold uncertainty score0.464

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 head0.007
GPT teacher head0.245
Teacher spread0.239 · 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