2020/06/30 by Shady E. Ahmed, Omer San, Kursat Kara +3
Computer Science · Earth and Planetary Sciences · Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Closure (psychology) #Computer science #Distributed computing #Domain (mathematical analysis) #Embedding #Engineering #Human–computer interaction #Interface (matter) #Lattice Boltzmann Simulation Studies #Mathematics #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks #Multiphysics #Parallel computing #Physical law #Programming language #Set (abstract data type) #Theoretical computer science #cs.LG #physics.comp-ph #physics.flu-dyn
paper · pdf · doi:10.1103/physreve.102.053304
published as Phys. Rev. E 102, 053304 (2020)
arxiv created 2020/11/01 · openalex publication_date 2020/11/13 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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 toward 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.