Scaling Relation

Scaling relations describe the relationships between different properties of a system, often exhibiting power-law or log-linear dependencies. Current research focuses on improving the accuracy and robustness of these relations, particularly in complex systems like large language models and astrophysical phenomena, using machine learning techniques such as symbolic regression and random forests to identify optimal predictors and reduce scatter. These improved scaling relations offer more precise estimations of key parameters (e.g., mass, performance) and enhance our understanding of underlying physical processes, impacting fields ranging from cosmology to artificial intelligence.