rule and constraint encoding for simulators

Model fidelity—the degree of correspondence between simulation and reality—lacks a formal, axiomatic foundation in digital engineering, resulting in ambiguous evaluation criteria and poor cross-domain comparability. Method: This paper introduces the first rigorous, verifiable theoretical framework for fidelity assessment, grounded in seven foundational axioms encompassing consistency, measurability, scale invariance, and other essential properties; the framework enables formal verification and comparative analysis of fidelity metrics. Empirical validation is conducted via integration into ground-vehicle modeling, demonstrating feasibility and practical guidance within existing evaluation paradigms. Contribution/Results: The work fills a critical theoretical gap in fidelity science and establishes a universal, standards-ready paradigm for fidelity assessment—directly advancing digital twin development, simulation verification and validation (V&V), and model-based systems engineering. It further provides a clear, principled roadmap for future methodological evolution and standardization.

This work addresses the behavioral gap between formal verification and actual execution in traditional engineering approaches, which often neglect execution semantics. To bridge this semantic divide, the paper proposes a Modeling and Simulation-Based Engineering (MSBE) methodology that explicitly treats execution semantics as a first-class engineering entity. It defines executability as the admissible model space induced by the stabilization of execution conditions and unifies model behavior with physical execution through an iterative cycle of formal execution, experimental execution, verification, and activity-mediated validation. Integrating formal methods, simulation-based verification, activity theory, and constraint modeling, MSBE establishes a general-purpose engineering framework applicable to diverse cyber-physical systems (CPS). The approach demonstrates its generality and effectiveness across four CPS categories: human-centric, biophysical, technological, and digital twin systems.

This work addresses the challenge that existing automatic code generation methods often produce structurally invalid or physically inconsistent models, which are unsuitable for engineering simulation. To ensure physical consistency and simulatability, the authors propose a procedural modeling framework that integrates domain knowledge injection, constraint-guided fine-tuning, and closed-loop simulation validation. Key contributions include CivilInstruct—the first instruction-following dataset tailored for structural engineering—along with a two-stage fine-tuning strategy and MBEval, a validation-driven evaluation benchmark. Experimental results demonstrate that the proposed approach significantly outperforms baseline methods across multiple rigorous metrics, effectively suppressing hallucinations and constraint violations, and enabling the direct use of generated models in structural dynamics simulations.

In software design, paradigm-implied semantic expectations—such as data abstraction consistency and feedback-control closed-loop behavior—are often left implicit, leading to design deviations and verification challenges. To address this, we introduce the concept of *design obligations*: explicit, logically formalizable, and verifiable specifications that codify such implicit constraints inherent to design paradigms. Leveraging formal modeling and paradigm semantics analysis, we establish two obligation frameworks—one for data-abstraction-based systems and another for feedback-driven adaptive systems—precisely capturing their core semantic requirements. We demonstrate that common design flaws stem from obligation violations and show how these obligations enable rigorous compliance verification and pedagogical application. This work bridges the semantic gap between design intent and implementation, providing both theoretical foundations and a methodological framework for paradigm-driven design assurance.

This study investigates the use of large language models (LLMs) to automatically translate neutral graph representations of fluid systems into high-quality, functionally correct code executable in mainstream simulation environments such as WNTR and Modelica. The authors systematically evaluate ten state-of-the-art LLMs combined with six prompting strategies across multiple benchmark scenarios, assessing generated code through software quality metrics and simulation fidelity. This work presents the first systematic comparison in the domain of fluid system modeling that examines how different LLMs and prompt engineering techniques influence both syntactic correctness and functional fidelity of generated simulation code, offering empirical guidance for model-driven code generation. Experimental results demonstrate that optimal configurations can produce syntactically valid code; however, a significant gap remains in achieving high simulation fidelity, highlighting key directions for future improvement.

This study addresses the limited semantic transparency and poor comprehensibility of existing conceptual models, which stem from their reliance on low-level syntactic constructs to represent domain abstractions, thereby hindering effective system design and stakeholder communication. To overcome this, the paper proposes a language-agnostic abstract symbol engineering approach that identifies, formalizes, visualizes, and validates recurring syntactic configuration patterns, replacing them with high-level, semantically transparent abstract symbols. The method is instantiated as the DeCleaR extension to Dynamic Condition Response (DCR) graphs. Empirical evaluation demonstrates that DeCleaR significantly enhances perceived model quality, pragmatic quality, and user preference compared to standard DCR graphs.