Bibliographic record
Abstract
In this PhD thesis we characterize quasi-optimal nonconforming methods for symmetric elliptic linear variational problems and investigate their structure. The abstract analysis is complemented by various applications and numerical tests in the finite element framework. In the first part of the thesis we introduce a rather large class of nonconforming methods, mimicking the variational structure of the model problem. Then, we characterize the subclass of quasi-optimal methods in terms of suitable notions of stability and consistency. We determine also the quasi-optimality constant and observe its dependence on the proposed notions of stability and consistency. For this purpose, we introduce an appropriate stability constant and two consistency measures. The second part of the thesis is devoted to exemplify the application of the above-mentioned results through the construction of various quasi-optimal nonconforming finite element methods. We consider the following three model problems: the Poisson problem, the linear elasticity problem and the biharmonic problem. For each one of them, we propose approximation methods based on discontinuous elements and/or classical nonconforming elements, such as the Crouzeix-Raviart and Morley elements. All methods are shown to be quasi-optimal, with quasi-optimality constant bounded in terms of shape regularity, and computationally feasible. In the third part of the thesis we restrict our attention to the two-dimensional Poisson problem and compare the performance of different quasi-optimal, first-order methods on various benchmarks. The purpose of these tests is twofold. On the one hand, we aim at assessing the actual size of the constants involved in our analysis. On the other hand, we highlight the importance of the proposed notions of stability and consistency when rough load terms come into play. All the numerical experiments are implemented within the finite element toolbox ALBERTA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".