ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Proceedings of the National Academy of Sciences, Volume 123, Number 1, January 2026.
Significance The increasing availability of complex and heterogeneous data sets poses significant challenges to traditional data-driven methods, which often assume data homogeneity and do not account for internal disparities. Quantifying entropy and its…