Extensions of the MapDE algorithm for mappings relating differential equations
Bibliographic record
Abstract
This paper is a sequel of our previous work in which we introduced the MapDE algorithm to determine the existence of analytic invertible mappings of an input (source) differential polynomial system (DPS) to a specific target DPS, and sometimes by heuristic integration an explicit form of the mapping. A particular feature was to exploit the Lie symmetry invariance algebra of the source, without integrating its equations, to facilitate MapDE, making algorithmic an approach initiated by Bluman and Kumei. In applications, however, the explicit form of a target DPS is not available, and a more important question is, can the source be mapped to a more tractable class. This aspect was illustrated by giving an algorithm to determine the existence of a mapping of a linear differential equation to the class of constant coefficient linear differential equations, again algorithmically realizing a method of Bluman and Kumei. Key for this application was the exploitation of a commutative sub-algebra of symmetries corresponding to translations of the independent variables in the target. In this paper, we extend MapDE to determine if a source nonlinear DPS can be mapped to a linear differential system. The methods combine aspects of the Bluman-Kumei mapping approach, together with techniques introduced by Lyakhov et al,for the determination of exact linearizations of ODE. The Bluman-Kumei approach which is applied to PDE, focuses on the fact that such linearizable systems must admit an infinite Lie subpseudogroup corresponding to the linear superposition of solutions in the target. In contrast, Lyakhov et al., focus on ODE, and properties of the so-called derived sub-algebra of the (finite) dimensional Lie algebra of symmetries of the ODE. We also illustrate the powerful maximal symmetry groups facility as a natural tool to be used in conjunction with MapDE.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".