AI RESEARCH
Data Enrichment for Symbolic Regression Using Diffusion Models
arXiv CS.LG
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ArXi:2606.00988v1 Announce Type: new Symbolic regression (SR) offers a route to scientific discovery by converting observations into interpretable governing equations. However, despite its promise, its reliability degrades sharply when spatiotemporal measurements are sparse, noisy, or physically incomplete, as commonly occurring in practice. Data enrichment (DE) has been shown to be able to mitigate this limitation, yet additional samples can mislead equation discovery unless they preserve the physical structure of the target system.