3 articles tagged with #symbolic-regression. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers developed new methods for extracting symbolic formulas from Kolmogorov-Arnold Networks (KANs), addressing a key bottleneck in making AI models more interpretable. The proposed Greedy in-context Symbolic Regression (GSR) and Gated Matching Pursuit (GMP) methods achieved up to 99.8% reduction in test error while improving robustness.
Researchers have developed a new framework that uses large language models to guide symbolic regression in discovering interpretable physical laws from high-dimensional materials data. The method reduces the search space by approximately 10^5 times compared to traditional approaches and successfully identified novel formulas for key properties of perovskite materials.
Researchers developed a physics-constrained machine learning framework that uses genetic programming to automatically discover closed-form mathematical equations for modeling water retention in porous materials with complex pore structures. The approach represents mathematical expressions as binary trees and incorporates physical constraints to ensure scientifically valid solutions.
