physics engine integration

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.

Physical engines (PEs) widely deployed in safety-critical systems—such as autonomous driving and medical robotics—frequently exhibit semantic-level physical failures, i.e., deviations from real-world physical behavior. However, existing testing approaches predominantly rely on white-box access and focus on crash detection, rendering them ineffective for identifying such subtle, semantics-driven failures. This paper presents the first large-scale empirical study to systematically characterize manifestations and root causes of physical failures in PEs, proposing the first fine-grained taxonomy. We comparatively evaluate diverse detection techniques and integrate deep learning, prompt engineering, and multimodal large language models to enable automated, semantic-level failure identification. We release PhysiXFails—an open-source benchmark dataset—and accompanying code, tools, and reproducible pipelines. Furthermore, informed by developer surveys, we propose actionable, deployable improvement strategies. Our work establishes a foundational framework—grounded in theory, empirical evidence, and practical implementation—for enhancing PE reliability.

This study addresses the challenge of intuitively representing complex interdependencies among simulation parameters in traditional tabular interfaces, which often lead to configuration errors and excessive cognitive load. To mitigate this, the work proposes the first application of an interactive Sankey diagram for visualizing parameter dependencies. A functional interface prototype was developed and evaluated against a conventional table-based approach using the PURE heuristic evaluation method, with a focus on user comprehension efficiency. Empirical results demonstrate that the Sankey diagram significantly reduces cognitive load by 51% and decreases interaction steps by 56%, thereby substantially enhancing the understandability and usability of configuration-intensive systems. This approach establishes a novel paradigm for visualizing parameter dependencies in complex simulation environments.

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 work addresses the challenges of poor cross-solver reusability, semantic inconsistency, and low interpretability of physics-based simulation models in engineering design. To this end, we propose the Physics Simulation Ontology (PSO), a domain-specific ontology for engineering design. PSO comprises two layers: PSO-Physics—built upon the Basic Formal Ontology (BFO)—formalizes solver-agnostic classical mechanical phenomena; PSO-Sim encapsulates solver-specific input specifications, thereby achieving semantic decoupling between physical modeling and numerical implementation. This constitutes the first systematic extension of BFO to the physics simulation domain. Using ontology engineering principles and OWL-based formalization, we validate PSO across two heterogeneous solvers, demonstrating significant improvements in modeling consistency, data reusability, provenance traceability, and cross-toolchain interoperability. The results confirm PSO’s effectiveness in enhancing the interoperability and reuse of simulation knowledge in engineering workflows.

This work addresses the lack of a modular desktop framework in scientific computing and engineering that supports orthogonal decoupling of 2D/3D visualization and simulation components. We propose and implement a modular multi-document interface framework tailored for the JVM ecosystem, which achieves architectural flexibility and long-term maintainability by decoupling the visualization layer, simulation engine, and an optional hardware-accelerated 3D rendering module. This design prevents 2D applications from incurring unnecessary 3D dependencies while enabling efficient synchronization between multiple views and simulations. Built on Java with a modular architecture and multithreaded model, the framework has been successfully integrated with a real-time 3D gas expansion simulation alongside synchronized 2D entropy map rendering. The implementation is publicly available on Maven Central, providing foundational support for the sustainable evolution of scientific software.

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.

Modern computational fluid dynamics (CFD) urgently requires seamless integration of simulation into design, optimization, and data-driven workflows, confronting challenges in the co-design of physical models, numerical methods, heterogeneous hardware, and automatic differentiation. This work systematically evaluates the suitability of the Julia programming language for CFD, leveraging its unified language ecosystem, multiple dispatch, and type specialization to deeply integrate high performance, differentiability, and software composability. Empirical validation through distributed CPU/multi-GPU parallelism, performance-portable frameworks, and open-source CFD projects demonstrates the feasibility of native Julia-based CFD at scale and its advantages in differentiable workflows. Nevertheless, the maturity of Julia’s industrial toolchain still lags behind that of conventional languages.

This study addresses the challenges of complex multiphysics system modeling—such as those encountered in nuclear engineering—where intricate model development, difficulties in sensor placement, and insufficient integration of real-world measurement data hinder accurate simulation. To overcome these issues, this work proposes a general and efficient data-driven reduced-order modeling framework implemented in Python. The framework is compatible with any solver that outputs VTK format and features a redesigned architecture that replaces DOLFINx with PyVista for mesh processing, numerical integration, and visualization. Field variables are uniformly stored as NumPy arrays, significantly enhancing usability and cross-platform compatibility. The resulting toolkit supports optimal sensor placement and seamless fusion of experimental measurements, thereby improving both understanding and computational efficiency in simulating complex multiphysics systems.