Mat2Boundary treats boundary conditions as sparse matrix-vector products and uses multi-stage compilation with polyhedral analysis to generate efficient matrix-free kernels and communication schedules for distributed block-structured PDE solvers.
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A typology of blended ML and physics-based modeling approaches for weather and climate is presented to support informed decision-making in prediction systems.
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Mat2Boundary: Treating User-Defined Boundary Condition as SpMV for Distributed PDE Solvers on Block-Structured Grids
Mat2Boundary treats boundary conditions as sparse matrix-vector products and uses multi-stage compilation with polyhedral analysis to generate efficient matrix-free kernels and communication schedules for distributed block-structured PDE solvers.
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Blending machine learning and physics-based approaches for weather and climate: a typology
A typology of blended ML and physics-based modeling approaches for weather and climate is presented to support informed decision-making in prediction systems.