Neural Symbolic Regression Uncovers Hidden Network Dynamics in Complex Systems
Researchers have developed a neural symbolic regression method that automatically discovers mathematical formulas governing network dynamics. The approach has corrected existing biological models and revealed universal patterns in epidemic transmission across different scales.
Breakthrough in Automated Network Analysis
Researchers have developed a neural symbolic regression approach that can automatically derive mathematical formulas from observational data of complex network systems, according to reports published in Nature Computational Science. The method addresses a fundamental challenge in complexity science: while vast amounts of observational data exist across numerous domains, mathematical models remain scarce outside a few well-understood areas with clear underlying principles.