AI Feynman: A physics-inspired method for symbolic regression
PulseAugur coverage of AI Feynman: A physics-inspired method for symbolic regression — every cluster mentioning AI Feynman: A physics-inspired method for symbolic regression across labs, papers, and developer communities, ranked by signal.
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Geometry-Constrained KANs Learn Adaptive Edge Functions for Symbolic Regression
Researchers have developed geometry-constrained Kolmogorov-Arnold Networks (KANs) that learn edge geometry through a scalar exponent 'p'. This approach allows for adaptive responses, ranging from sharp, threshold-like b…
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New DDRSR method enhances symbolic regression with broader applicability
Researchers have introduced Deep Divide and Reduce in Symbolic Regression (DDRSR), a novel method designed to improve the discovery of mathematical expressions from data. Unlike previous approaches that rely on brute-fo…
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Language models guide symbolic equation discovery in new LLM-PySR method
Researchers have developed a new method called LLM-PySR that uses language models to guide the discovery of scientific equations. Instead of having the language model directly propose equations, it controls the search p…