A Search for the Underlying Equation Governing Similar Systems

Changwei Loh; Daniel Schneegass; Pengwei Tian

A Search for the Underlying Equation Governing Similar Systems

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 1908.10673 v1 pith:M33S5EKI submitted 2019-08-27 cs.NE

A Search for the Underlying Equation Governing Similar Systems

Changwei Loh , Daniel Schneegass , Pengwei Tian This is my paper

classification cs.NE

keywords equationsystemsapproachgoverningcandidatesearchsimilarstructural

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original abstract

We show a data-driven approach to discover the underlying structural form of the mathematical equation governing the dynamics of multiple but similar systems induced by the same mechanisms. This approach hinges on theories that we lay out involving arguments based on the nature of physical systems. In the same vein, we also introduce a metric to search for the best candidate equation using the datasets generated from the systems. This approach involves symbolic regression by means of genetic programming and regressions to compute the strength of the interplay between the extrinsic parameters in a candidate equation. We relate these extrinsic parameters to the hidden properties of the data-generating systems. The behavior of a new similar system can be predicted easily by utilizing the discovered structural form of the general equation. As illustrations, we apply the approach to identify candidate structural forms of the underlying equation governing two cases: the changes in a sensor measurement of degrading engines; and the search for the governing equation of systems with known variations of an intrinsic parameter.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing
cs.LG 2026-01 conditional novelty 6.0

Introduces partially-shared parameters for symbolic regression with multiple categorical variables, matching prior fit quality on a supernovae dataset with fewer parameters.