arXiv:2606.00988v1 Announce Type: new Abstract: 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. Such implication of DE requires narrow domain exp
The paper leverages recent advancements in diffusion models to address a long-standing challenge in symbolic regression, reflecting a current trend in applying generative AI to scientific discovery.
Improving symbolic regression's reliability in sparse or noisy data environments could significantly accelerate scientific discovery across various fields by generating more accurate and interpretable governing equations.
The ability to generate physically consistent synthetic data through diffusion models changes how researchers can tackle the data scarcity and quality issues common in scientific observation, potentially lowering the barrier to entry for complex physical modeling.
- · AI researchers
- · Scientific research institutions
- · Drug discovery
- · Materials science
- · Traditional data augmentation methods
- · Domain experts reliant on extensive manual data collection
More robust and accurate models will be developed from limited or imperfect experimental data.
Accelerated discovery of new physical laws, chemical processes, and biological mechanisms becomes more feasible.
This could lead to a 'democratization' of complex scientific modeling, enabling smaller labs or less-resourced teams to make significant contributions.
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Read at arXiv cs.LG