partitioned model coupling

This study addresses the challenges of model reusability and cross-scale coupling in computational biomechanics by proposing a partitioned coupling framework that enables collaborative simulation of independent FEBio models across disparate time scales. In this approach, a primary model governs slow processes while an auxiliary model efficiently resolves fast responses and feeds results back to the primary domain. By decoupling coupling logic from the solver implementation, the framework facilitates reproducible and maintainable multiphysics workflows without requiring modifications to the underlying codebase. It supports bidirectional variable exchange, spatial mapping, and user-defined filtering. The method successfully reproduces benchmark solutions and demonstrates its efficacy in modeling chemo-mechanical cartilage damage and mechano-biological interactions during bone healing.

This work proposes a scalable parallel interpolation algorithm to address the challenge of data consistency across multiple solvers in overlapping grid regions for atmospheric wave simulations. The method enables efficient, in situ data coupling between solvers by introducing a grid-coupling mechanism tailored for large-scale parallel environments, ensuring global solution consistency while balancing computational efficiency and communication overhead. Evaluated in realistic atmospheric wave simulation scenarios, the algorithm demonstrates strong scalability and high performance, significantly improving both the accuracy and efficiency of data exchange among multiphysics solvers.

This work addresses the challenge that existing frameworks struggle to efficiently support the direct development of multiphysics coupling solvers and parallel adaptive simulations. We propose a portable, reproducible cross-language framework that, for the first time, tightly integrates Trixi.jl with deal.II to construct a strongly coupled partitioned solver for Newtonian self-gravitating hydrodynamics. By combining a strong coupling strategy, cross-language interoperability, adaptive mesh refinement, and parallel computing, our framework substantially simplifies the development of coupled solvers. Numerical experiments demonstrate high-order convergence, physical consistency, and effective adaptivity of the algorithm, while also exhibiting favorable strong scaling performance in parallel execution.

This work addresses the challenges of high-fidelity simulation in fusion energy systems—specifically, geometric modeling, multiphysics coupling, and the integration of particle and continuum methods—by proposing a unified framework that seamlessly combines commercial CAE software with existing fusion codes. The framework enables accurate representation of complex geometries, automatic generation of unstructured meshes, and efficient coupling between particle transport and continuum solvers. The resulting simulation workflow significantly enhances geometric fidelity for critical components and strengthens capabilities in multiscale, multiphysics co-simulation, while maintaining strong scalability and computational efficiency.

This study addresses the challenge of achieving both accuracy and conservation in field variable transfer within black-box multiphysics coupling when source mesh information is unavailable. The authors propose a novel field transfer method based on stochastic approximation Galerkin projection, which, for the first time, integrates stochastic approximation into a black-box coupling framework. This approach enables asymptotic conservation and high accuracy without requiring access to the source mesh. By overcoming the limitations of conventional radial basis function and mesh intersection methods, and leveraging GPU parallel acceleration (NVIDIA A100), the proposed technique demonstrates superior performance on both standard domains and the LTX fusion reactor mesh—exhibiting lower conservation error, higher accuracy, and computational cost comparable to that of mesh intersection methods.

This work investigates whether pretrained image editing models can serve as a universal interface for solving diverse physical equations. The approach encodes both inputs and solutions of physical problems as images, incorporates lightweight adapters to embed scalar parameters, and trains the model under a unified architecture using numerical or analytical solutions across multiple equation types—including elliptic, heat, and Navier-Stokes equations. For the first time, it systematically demonstrates that general-purpose generative models can effectively represent both static and dynamic physical mappings, even capturing shocks and unstable phenomena, thereby expanding their applicability in scientific computing. Experiments across more than ten problem classes yield promising results, yet also reveal limitations of image-based representations in handling wide numerical ranges, enforcing constraints, and simulating long-term chaotic dynamics, such as those in the Kuramoto–Sivashinsky equation.

This work addresses the frequent mismatch between user-specified physical intent and the actual behavior of multiphysics simulation code generated by large language models, often due to erroneous implementations of partial differential equations (PDEs). To bridge this gap, we propose a PDE-structure-based intent verification method that deterministically reconstructs the governing equations implicitly encoded in the generated code and compares them against the user’s intended PDEs, enabling semantic correctness validation and iterative refinement. We introduce, for the first time, a formal metric termed the Intent Fidelity Score (IFS) to quantify alignment with physical intent, establish a PDE-driven feedback loop, and demonstrate compatibility with major PDE frameworks including MOOSE, FEniCS, and FreeFEM. Evaluated on 220 cases in MooseBench, our approach substantially improves IFS—by 0.22–0.41 on challenging instances with initial IFS < 0.7—while audits reveal that execution-only repair strategies still yield physically incorrect results in 39–40% of cases.

This study addresses numerical instabilities arising in fluid flow simulations on three-dimensional terrain when using staggered grids with embedded boundaries and small grid cells. The work presents the first adaptation of the embedded boundary method to a staggered grid framework, introducing distinct geometric treatments for velocity components—defined on cell faces—and thermodynamic scalar fields—defined at cell centers. To mitigate small-cell instability, the weighted state redistribution (WSRD) scheme is extended into this framework. Integrated within the ERF model alongside adaptive mesh refinement and performance-portable implementation, the proposed approach demonstrates excellent agreement with terrain-following coordinate simulations, achieving both high accuracy and robust numerical stability.