We propose a domain-decomposed method for Multivariate Functional Approximations (MFA) using B-spline bases, enhancing scalability and accuracy in large datasets. Our approach minimizes local errors and recovers high-order continuity at subdomain interfaces, optimizing communication costs. Performance results demonstrate its efficiency and scalability across various datasets in 1D, 2D, and 3D.

Coupled Earth System Models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity and local feature preservation of four different remapper algorithms, for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.

Accurate climate modeling of coupled Earth systems requires mapping of solution field data between dependent components that use non-matching discrete meshes. While existing workflows provide a pathway to generate the projection weights as an offline step, severe bottlenecks impede flexible setup of high-resolution models. In this paper, we present new algorithmic approaches to simplify the E3SM computational workflow using a scalable software infrastructure to generate the remapping operators.

An integrated multi-physics simulation capability for the design and analysis of current and future nuclear reactor models is being investigated, to tightly couple neutron transport and thermal-hydraulics physics under the SHARP framework. Over several years, high-fidelity, validated mono-physics solvers with proven scalability on petascale architectures have been developed independently. Based on a unified component-based architecture, these existing codes can be coupled with a mesh-data backplane and a flexible coupling-strategy-based driver suite to produce a viable tool for analysts.

The modeling of nuclear reactors involves the solution of a multi-physics problem with widely varying time and length scales. This translates mathematically to solving a system of coupled, non-linear, and stiff partial differential equations (PDEs). A multi-physics framework, KARMA (K(c)ode for Analysis of Reactor and other Multi-physics Applications), is presented. KARMA uses tight coupling strategies for various physical models based on a Matrix-free Nonlinear-Krylov (MFNK) framework in order to attain high-order spatio-temporal accuracy for all solution fields in amenable wall clock times, for various test problems.