Data-driven Progressive Discovery of Physical Laws
Symbolic regression is a powerful tool for knowledge discovery, enabling the extraction of interpretable mathematical expressions directly from data. However, conventional symbolic discovery typically follows an end-to-end, "one-step" process, which often generates lengthy and physically meaningless expressions when dealing with real physical systems, leading to poor model generalization. This limitation fundamentally stems from its deviation from the basic path of scientific discovery: physical laws do not exist in a single form but follow a hierarchical and progressive pattern from simplicity to complexity. Motivated by this principle, we propose Chain of Symbolic Regression (CoSR), a n...
| Authors | Mingkun Xia, Weiwei Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 29 May 2026 |
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