integrate multiphysics simulations

Weakly coupled multiphysics problems—such as evaporative cooling of electric motors in aerospace applications—pose challenges for conventional tightly coupled simulation frameworks due to disparate time scales and physical mechanisms. Method: This paper proposes a physics-event-triggered loosely coupled solution framework, wherein individual physics fields advance independently and synchronize only upon occurrence of critical physical events—e.g., abrupt residual changes, phase-interface migration, or significant geometric deformation—replacing traditional fixed-time-step coupling. The framework integrates independent field solvers and employs adaptive synchronization criteria based on residuals, geometry, and unknown-field variations. Contribution/Results: Validated on evaporative cooling simulations for UAV and eVTOL motor systems, the method achieves a 37% speedup over conventional tight coupling while reducing numerical error by an order of magnitude. It delivers high accuracy, strong robustness, and superior physical fidelity, establishing a scalable new paradigm for weakly coupled multiphysics simulation.

Existing machine learning solvers—such as PINNs, FNOs, and DeepONets—excel on single-field PDEs but lack systematic evaluation and methodological adaptation for strongly coupled multiphysics systems governed by multi-physics PDEs. To address this gap, we introduce Multiphysics Bench, the first general-purpose benchmark dataset for strongly coupled multiphysics PDEs, comprising 12 canonical problem classes. Through rigorous evaluation, we identify a critical failure mode: mainstream methods suffer significant performance degradation due to inadequate modeling of inter-field coupling. We propose three targeted strategies—loss reweighting, gradient coordination, and cross-field information interaction—to mitigate this limitation. Our experiments deliver reproducible baselines, failure-mode analysis, and 12 actionable guidelines. This work establishes the first comprehensive benchmark and methodology framework for multiphysics scientific machine learning, bridging the gap between current solvers and real-world complex physical systems.

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 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 proposes the first autonomous simulation system that integrates an agent-based architecture with domain-finetuned large language models (LLMs) to enable end-to-end modeling and solution of solid mechanics, fluid dynamics, and multiphysics problems. Addressing the limitations of conventional LLMs—which often hallucinate, lack awareness of variational structures, and fail to close the loop from problem description to verified solutions—the system incorporates retrieval-augmented multi-LLM code generation and filtering, finetuned models spanning 3B to 120B parameters, multi-agent collaboration, and runtime feedback mechanisms. A high-quality corpus of over a thousand FEniCS codes was curated to support training and evaluation. On a benchmark suite of 39 nonlinear elasticity, plasticity, and non-Newtonian fluid problems, the GPT OSS 120B model achieved a code generation success rate of 71.79%, substantially outperforming non-agent-based approaches.

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 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 implicit Finite Operator Learning (iFOL), a physics-informed operator learning framework for multiphysics problems governed by coupled partial differential equations on arbitrary domains, which operates without requiring ground-truth labeled data. Built upon the finite element weighted residual formulation, iFOL establishes a resolution-independent mapping from input parameters to the solution space and provides a unified treatment of complex geometries and multiphysics coupling. Implemented within the Folax system on the JAX platform, the approach integrates FNO, DeepONet, and iFOL, leveraging finite element residuals to construct physics-constrained loss functions. Experiments demonstrate that iFOL achieves high efficiency on complex geometries in two- and three-dimensional nonlinear thermo-mechanical coupling and industrial casting scenarios, while FNO excels in accuracy on regular domains; furthermore, a single-network end-to-end training strategy significantly outperforms baseline 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.