Learning missing physics from legacy simulators with alternating neural integrators (opens in new tab)

A recurring challenge in science and engineering is the model–reality gap, where trusted legacy simulators lose fidelity due to unresolved physics or structural incompleteness. This challenge has motivated remedies ranging from imperfect mechanistic models to fully data-driven surrogates. Here, we address this gap with Alternating Neural Integrators (ANI), a non-intrusive reuse-and-correct framework for upgrading executable legacy simulators without requiring access to or modification of thei...