MOVING MESH METHODS FOR MOVING BOUNDARY PROBLEMS AND HIGHER ORDER PARTIAL DIFFERENTIAL EQUATIONS
Bibliographic record
Abstract
This thesis studies the moving mesh method and its applications in the numerical solution of moving boundary problems and higher order evolutionary partial differential equations. The concept of equidistribution has played a fundamental role in moving mesh methods. For a given adaptation function, de Boor's algorithm is commonly used for generating equidistributing meshes. The algorithm produces a sequence of meshes upon using piecewise constant interpolation for the adaptation function on the current mesh and generating a new mesh that exactly equidistributes the interpolant. Although the effectiveness of this algorithm was confirmed numerically long ago, the proof for the existence of the limit mesh and the convergence of this algorithm have thus far remained theoretically elusive. These theoretical issues are treated in Chapter 2 of this thesis. Numerical results are also given to illustrate the theoretical findings as well as stopping criteria necessary for the implementation of the algorithm. The use of moving meshes has become a popular technique for improving existing approximation schemes for moving boundary problems. In Chapter 3, we study the relative efficiency and accuracy of various numerical methods for moving boundary problems on moving meshes. A moving mesh front-tracking method based on equidistributing a specially designed adaptation function is proposed for moving boundary problems of implicit type. The resulting numerical method does not require any analytical knowledge of solutions, assumptions on solution profiles or interpolation/extrapolation which are common in other methods in the literature. Some preliminary work for moving mesh front-tracking methods in two dimensions is presented in Chapter 4. Finally, MOVCOL4, a moving mesh collocation code specifically designed for solving general fourth-order evolutionary partial differential equations, is analyzed in Chapter 5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".