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Record W6979722301

Absolutely continuous invariant measures for piecewise
\nconvex maps of an interval with countable (infinite)
\nnumber of branches

2023· dissertation· en· W6979722301 on OpenAlexfundno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Languageen
FieldMathematics
TopicMathematical Dynamics and Fractals
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsAbsolute continuityCountable setPiecewiseRegular polygonInvariant (physics)Measurable functionUniquenessConvex functionSequence (biology)Continuous function (set theory)
DOInot available

Abstract

fetched live from OpenAlex

This thesis delves into three areas of research on dynamical systems. First, it explores the existence and exactness of Absolutely Continuous Invariant Measures (ACIM) for piecewise convex maps with countable (infinite) number of branches. Second, it employs Ulam’s method to approximate the density function of these ACIMs. Third, it investigates the existence of Absolutely Continuous Invariant Measures for piecewise concave maps using the technique of conjugation. For the first topic, we examine the existence and uniqueness of ACIMs within two distinct classes, denoted as T ∞ \npc (I) and T ∞,0 pc (I), which together encompass piecewise convex maps τ : I =[0, 1] → [0, 1] with countable number of branches. We establish the necessary conditions under which these maps possess a unique ACIM, presenting multiple illustrative examples of ACIM existence. \nOur findings are based on the analysis of the Frobenius-Perron operator associated with these maps, utilizing analytical techniques to gain insights into the Frobenius-Perron operator’s properties. \nThe main purpose of the second part of this thesis is to approximate τ by the map τn, where we construct a sequence τn with a finite number of branches. Then, approximate τn by Ulam’s method. Since piecewise convex maps have countable (infinite) number of branches, the convergence of Ulam’s method becomes more challenging, and complexity makes it harder to find a suitable sequence of approximating functions that can accurately analyze the behavior of this system across all branches. \nThe primary contribution of this Ph.D. thesis lies in the generalization of the existence of absolutely continuous invariant measures for piecewise convex maps defined on an interval with an infinite number of branches. In the case of T ∞pc (I), we examine piecewise convex maps with an infinite number of branches and arbitrary countable number of limit points for partition points separated from 0. For T ∞,0pc (I), we consider piecewise convex maps with countable number of branches and partition points that converge to 0. Throughout the thesis, we investigate Absolutely Continuous Invariant Measures (ACIM) for τ ∈ T ∞pc (I) and τ ∈ T ∞,0pc (I), along with exploring non-autonomous dynamical systems of maps within these classes and scrutinize the existence of ACIMs for their limit maps. \nFurthermore, we investigate the approximation for ACIMs associated with piecewise convex maps with an infinite number of branches by employing Ulam’s method. This computational approach is a practical way to estimate the density functions of ACIMs and thereby facilitate their numerical analysis. We then extended our research area on ACIM for piecewise concave maps with countable number of branches. We examine the existence and uniqueness of ACIMs for two distinct classes, T ∞pcv(I) and T ∞,1pcv (I), which encompass piecewise concave mappings denoted as σ. We utilize the concept of conjugation with piecewise convex maps τ to demonstrate that σ conserves a normalized absolutely continuous invariant measure with a density that exhibits increasing behavior.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.308
Teacher spread0.256 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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