modeling

In the pre-prototype phase of complex novel systems, the absence of empirical data impedes rigorous assessment of simulation model credibility. Method: This paper proposes a physics-fidelity-based model trust evaluation method that bypasses reliance on real-world measurements. Instead, it quantifies model applicability by systematically analyzing the completeness of represented physical phenomena, the mathematical complexity of their formulation, and the fidelity of emergent behavior modeling. Contribution/Results: The approach enables objective, quantitative ranking of multiple candidate models under data-scarce conditions—thereby significantly enhancing the reliability of simulation-driven decisions during early-stage design. It establishes both theoretical foundations and practical tools for model-based design in high-uncertainty scenarios, advancing trustworthy digital twin development and physics-informed simulation validation.

Existing mathematical modeling lacks a rigorous, unambiguous ontological foundation, hindering a unified characterization of the mapping between models and real-world phenomena. This paper introduces, for the first time, an axiomatic definition of mathematical models grounded in Hilbert-space operator theory: a model is formalized as a computable operator acting on random variables, systematically unifying theoretical derivation, experimental implementation, and statistical identification. We further establish a geometric correspondence between the model manifold and the prediction surface, exposing intrinsic structural properties and the fundamental nature of model computability. This framework fills a critical gap in the formal ontology of modeling, providing a unified mathematical foundation for interdisciplinary model construction. It significantly enhances the logical rigor of theoretical inference and the reliability of empirical validation.

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 model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

This paper systematically examines the structural role and evolutionary trajectory of simulation methods across the statistical lifecycle. Addressing the current fragmentation and conceptual ambiguity in simulation practice, the study introduces, for the first time, a comprehensive functional taxonomy—spanning model specification, diagnostic checking, validation, and inference—and proposes a “simulation-driven” paradigm for statistical practice, prioritizing computational scalability. Methodologically, it integrates Monte Carlo simulation, approximate Bayesian computation (ABC), simulation-based calibration, and posterior predictive checking, implemented via high-performance computing frameworks to enable large-scale empirical analysis. Key contributions are: (1) establishing simulation as foundational statistical infrastructure; (2) providing an actionable roadmap for algorithm design, statistical software development, and pedagogical reform; and (3) advancing a paradigm shift in statistical practice—from model-centric to simulation-augmented inference.

This work addresses the opacity of existing large language model (LLM)-driven simulation-based decision systems, which treat scientific simulators as black boxes and lack explicit reasoning about their underlying mechanisms and assumptions. To overcome this limitation, the authors propose MechSim, a novel framework that introduces mechanism-level reasoning into the interaction between LLMs and scientific simulators. MechSim employs structured mechanistic representations to model a simulator’s assumptions, variable dependencies, and execution traces, integrating neural-symbolic reasoning with a constraint engine to enable LLMs to perform explainable, traceable, and constraint-aware inference. Experiments across multiple high-stakes domains demonstrate that MechSim significantly enhances the quality of mechanistic explanations, deepens simulation analysis, and improves the reliability of downstream decisions, thereby transcending the traditional limitation of neural-symbolic systems that operate only on static symbolic representations.

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 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.

This work addresses the semantic fragmentation and toolchain fragmentation in traditional model-driven engineering, which stem from the lack of a unified formal foundation among models, metamodels, templates, and transformations. To bridge this gap, the paper introduces Model Expression Algebra, treating models as values and expressions as terms, and unifying modeling operations through an evaluation homomorphism. By embedding a domain-specific language (DSL), the approach integrates metamodeling, model construction, and transformation within a single functional algebraic framework—unifying all four aspects for the first time. A type system ensures transformation safety, while free variables represent templates and computational operators encode functional logic, enabling type-preserving evaluation and built-in support for large-model expressions. Experimental results demonstrate that a single language can fulfill the full spectrum of modeling tasks while providing formal guarantees.

This work addresses the challenge of behavioral inconsistency in automatically generated BPMN models due to semantic ambiguity in natural language process descriptions. It proposes the first closed-loop diagnosis and repair framework that operates without requiring ground-truth BPMN annotations. By analyzing the distribution of key performance indicators (KPIs) across multiple model generations, the approach identifies behavioral variations and employs model-based diagnostic techniques to pinpoint gateway logic discrepancies. These discrepancies are traced back to specific source text fragments, which are then refined through an evidence-driven textual revision process. Evaluated on clinical guidelines for diabetic kidney disease management, the method significantly reduces behavioral variability in regenerated models and enhances the semantic stability of executable process models, establishing an end-to-end mapping from behavioral inconsistency to targeted textual correction.