Accurate approximate solution of partial differential equations at off-mesh points
Bibliographic record
Abstract
Numerical methods for partial differential equations often determine approximations that are more accurate at the set of discrete meshpoints than they are at the “off-mesh” points in the domain of interest. These methods are generally most effective if they are allowed to adjust the location of the mesh points to match the local behavior of the solution. Different methods will typically generate their respective approximations on incompatible, unstructured meshes, and it can be difficult to evaluate the quality of a particular solution, or to visualize important properties of a solution. In this paper we will introduce a generic approach which can be used to generate approximate solution values at arbitrary points in the domain of interest for any method that determines approximations to the solution and low-order derivatives at meshpoints. This approach is based on associating a set of “collocation” points with each mesh element and requiring that the local approximation interpolate the meshpoint data and almost satisfy the partial differential equation at the collocation points. The accuracy associated with this interpolation/collocation approach is consistent with the “meshpoint accuracy” of the underlying method. The approach that we develop applies to a large class of methods and problems. It uses local information only and is therefore particularly suitable for implementation in a parallel or network computing environment. Numerical examples are given for some second-order problems in two and three dimensions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".