REVIEW 1 cited by
Practical Guide to the Symbolic Computation of Symmetries of Differential Equations
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Symmetries play an critical role in finding analytic solutions to nonlinear differential equations. A symmetry is a mapping of the solutions of the differential equation into the solutions and have been studied extensively for over a century. We present a computational approach to finding symmetries and computer algebra programs to compute the usually very large system of determining partial differential equations. We also provide computer algebra algorithm that at least automatically solves most of these equations and in simple cases provides a complete solution. The algorithms are programmed in maxima/wxmaxima that is freely available.
Forward citations
Cited by 1 Pith paper
-
LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries
LieSolver fits initial/boundary data with linear combinations of Lie-symmetry-generated base solutions, enforcing linear homogeneous PDEs exactly by construction.
Discussion (0). Continue with ORCID to comment.