arXiv subjects

Hessam S. Sarjoughian

Publications and source records attributed to Hessam S. Sarjoughian.

5 recordsLinked to original sources

arXiv2026

Constraint-Driven Modeling Enabling Dual Model Checking and Simulation for Discrete Event Systems

Verification and validation (V&V) are crucial methods for evaluating the requirements and specifications of dynamical models that fulfill their intended purposes. Parallel Discrete EVent System Specification (PDEVS) is a system-theoretic modeling approach for creating modular, hierarchical component-based simulation models. In this paper, we introduce Constraint-DEVS, a method for creating bounded Parallel DEVS models that lend themselves, in addition to simulation, to model checking. We extend the DEVS-Suite framework to create Constraint-DEVS specifications which can then be model checked using a proposed state exploration protocol with the Parallel DEVS abstract simulator protocol. These capabilities, along with the support for non-determinism, complex data transfer, and performance-related property checking, make Constraint-DEVS and its accompanying DEVS-Suite a unique framework for the development, verification, and validation of discrete-event systems. In order to demonstrate this work, we developed and verified models of Network-on-Chip. Also, we detail behavioral design artifacts for the DEVS-Suite framework's hybrid model-checking and simulation engine.

cs.SE↗

arXiv2026

LLM-based Framework for Generating and Verifying Parallel DEVS Statecharts

The development of models demands sound modeling and simulation knowledge as well as domain knowledge. Every model should accurately represent a system's dynamics and be verifiable. Toward this objective, this research introduces an agentic PDEVS-LLM framework to assist human modelers in generating and verifying PDEVS statecharts for behavior modeling of atomic Parallel Discrete Event System Specification (PDEVS) models. The framework supports (re)generating plausible facts from a system description prompt using the agentic LLM used for generating plausible facts. Inconsistencies in plausible facts lead to incorrect PDEVS statecharts having logical structure and behavioral inaccuracies. A controlled-correction mechanism is developed to verify the logical consistency of the plausible facts. The agentic LLM is used to generate key behavioral conditions from the system description prompt. The plausible facts are then verified against the behavioral conditions using propositional logic entailment for a finite number of times. The verification results enable the generation of modification prompts that can reduce errors in generated plausible facts, resulting in more accurate PDEVS statecharts. To verify a statechart's logical correctness, its Timed Automata counterpart is manually created and verified for deadlock and reachability properties. The human modeler may regenerate plausible facts and PDEVS statecharts iteratively and incrementally. A basic correctness metric is introduced to quantify the completeness and accuracy of the expected behavioral traits of the PDEVS statechart models. A collection of example systems with varying levels of complexity is developed to demonstrate the capabilities and limitations of LLMs. The evaluation of the proposed verification mechanism shows a substantial improvement in the logical consistency of generated statecharts.

cs.LG↗

arXiv2024

A Benchmark Time Series Dataset for Semiconductor Fabrication Manufacturing Constructed using Component-based Discrete-Event Simulation Models

Advancements in high-computing devices increase the necessity for improved and new understanding and development of smart manufacturing factories. Discrete-event models with simulators have been shown to be critical to architect, designing, building, and operating the manufacturing of semiconductor chips. The diffusion, implantation, and lithography machines have intricate processes due to their feedforward and feedback connectivity. The dataset collected from simulations of the factory models holds the promise of generating valuable machine-learning models. As surrogate data-based models, their executions are highly efficient compared to the physics-based counterpart models. For the development of surrogate models, it is beneficial to have publicly available benchmark simulation models that are grounded in factory models that have concise structures and accurate behaviors. Hence, in this research, a dataset is devised and constructed based on a benchmark model of an Intel semiconductor fabrication factory. The model is formalized using the Parallel Discrete-Event System Specification and executed using the DEVS-Suite simulator. The time series dataset is constructed using discrete-event time trajectories. This dataset is further analyzed and used to develop baseline univariate and multivariate machine learning models. The dataset can also be utilized in the machine learning community for behavioral analysis based on formalized and scalable component-based discrete-event models and simulations.

cs.LG↗

arXiv2021

Simulation, Model Checking, and Execution of Activity Models

This paper presents our findings for using activity modeling for simulation (validation), model checking (verification), and execution purposes. Each is needed to tackle system complexity and further research into behavioral modeling. We argue different models implicate different understandings and expectations. We emphasize some distinctions with demonstrations using the Discrete Event System Specification with an exemplary model. In particular, the continuous-time base in models helps observe some inherent limitations and strengths in acquiring each capability. The temporal characterization of input, output, and state, or the lack thereof, is at the core of developing behavioral specifications. We use DEVS to arrive at the capability of validating simulations for activity models. We use Constrained-DEVS for the verification of activity models. We show how some executions can be derived, whether from the specification itself or with considerations for simulation and model checking.

cs.SE↗

arXiv2018

Challenges of Achieving Efficient Simulations Through Model Abstraction

Coupled natural systems are generally modeled at multiple abstraction levels. Both structural scale and behavioral complexity of these models are determinants in the kinds of questions that can be posed and answered. As scale and complexity of models increase, simulation efficiency must increase to resolve tradeoffs between model resolution and simulation time. From this vantage point, we will show some problems and solutions by using as example a vegetation-landscape model where individual plants belonging to different species are represented as collectives that undergo growth and decline cycles spanning hundreds of years. Collective plant entities are assigned to cells of a static, two-dimensional grid. This coarse-grain model, guided by homomorphic modeling ideas, is derived from a fine-grain model representing plants as individual objects. These models are developed using Python and GRASS tools. A set of experiments is devised to reveal some barriers in modeling and simulating this class of systems.

cs.CE↗