Nature Communications, Published online: 23 June 2026; doi:10.1038/s41467-026-74002-2 Alternating Neural Integrators (ANI) offer a non-intrusive “reuse-and-correct” framework to upgrade legacy simulators by alternating prior model evolution with learned neural corrections, improving fidelity in complex systems and enabling interpretable, data-driven refinement.
Learning missing physics from legacy simulators with alternating neural integrators
Kailiang Wu

