  Complex natural or engineered systems comprise multiple characteristic
scales, multiple spatiotemporal domains, and even multiple physical closure
laws. To address such challenges, we introduce an interface learning paradigm
and put forth a data-driven closure approach based on memory embedding to
provide physically correct boundary conditions at the interface. To enable the
interface learning for hyperbolic systems by considering the domain of
influence and wave structures into account, we put forth the concept of upwind
learning towards a physics-informed domain decomposition. The promise of the
proposed approach is shown for a set of canonical illustrative problems. We
highlight that high-performance computing environments can benefit from this
methodology to reduce communication costs among processing units in emerging
machine learning ready heterogeneous platforms toward exascale era.
