simulation

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.

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.

Long-standing deficiencies in standardized, high-quality criteria for data science simulation studies have led to inconsistent design practices, poor reproducibility, and limited external validity. To address this, we propose MERITS—a simulation quality framework for trustworthy data science—systematically defining six core dimensions: Modularity, Efficiency, Realism, Stability, Intuitiveness, and Transparency. MERITS is the first to operationalize the PCS (Predictability-Computability-Stability) theory into concrete design principles and innovatively introduces a “cooking metaphor” to structure simulations as executable “recipes.” The framework includes 13 actionable design guidelines and is validated through empirical reconstruction of existing studies. Designed for cross-disciplinary applicability, MERITS has been successfully applied to diagnostic reconstructions of prior work, yielding substantial improvements in interpretability, reproducibility, and external validity.

Doctoral students in life sciences commonly lack formal software engineering training, hindering the development of robust, reproducible, and collaborative research software. Method: This study proposes ten pedagogical principles for research software development, establishing the first systematic framework centered on “research software pedagogy”—distinct from generic programming instruction. It integrates software engineering best practices (e.g., Git-based version control, CI/CD pipelines, unit testing, RESTful API design), learning science principles, and authentic research workflows, emphasizing the seamless embedding of automation, documentation, testing, and collaborative practices throughout the research lifecycle. Contribution/Results: The framework delivers a generalizable, plug-and-play pedagogical paradigm. Deployed across multiple Chinese universities’ life sciences PhD programs, it has demonstrably improved software deliverable quality, code reusability, and cross-team collaboration efficiency—bridging critical gaps between computational literacy and rigorous, team-based scientific software practice.

This study addresses the lack of systematic synthesis at the intersection of artificial intelligence (AI) and modeling and simulation (M&S) by proposing, for the first time, a structured framework based on the full M&S lifecycle—encompassing model construction, input modeling, execution, experimentation, validation, and output analysis. It elucidates the bidirectional integration mechanisms between AI and simulation: how AI enhances or substitutes traditional simulation components, and how simulation supports AI training and evaluation. Incorporating generative AI technologies such as large language models, the paper identifies representative application paradigms and integration approaches across each phase, synthesizes key achievements, and presents a conceptual roadmap tailored to the rapidly evolving ecosystem, while highlighting current limitations and open research challenges.

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 study addresses a prevalent conflation in reinforcement learning research between two distinct objectives involving simulators: solving the simulator as an end in itself versus treating it as a proxy for a real-world deployment environment. The former seeks high returns within the simulated domain, while the latter aims to transfer learned policies to the physical world. These goals entail fundamentally different algorithmic constraints, methodological requirements, and evaluation criteria. Through conceptual clarification, illustrative case studies, and controlled experiments, this work exposes the pitfalls arising from conflating these roles, delineates their respective appropriate use cases, and calls upon the research community to align experimental design, evaluation metrics, and algorithm development with the intended purpose of simulator usage.