SciML in the Wild: Structural Priors Can Hurt When Mismatched

Jul 14, 2026

A new preprint evaluates Scientific Machine Learning (SciML) methods for macroeconomic forecasting and finds that structural priors can act as misregularizers when they do not align with the true data-generating process. In tests across 23 countries, less-constrained models like ARIMA and Neural ODEs consistently outperformed more-constrained models such as PINNs and UDEs. The study highlights the challenges of low-frequency macroeconomic prediction and cautions that more structural assumptions do not always yield better results.

Why it matters: This work challenges the assumption that adding structural priors always improves model performance, offering important guidance for SciML practitioners.

Full story at: arXiv Machine Learning ↗