Aims

Journal of Hybrid Modelling and Intelligent Engineering Systems (JHMIES) is an international, peer-reviewed, open-access journal devoted to research on hybrid modelling approaches within intelligent engineering systems.

Contemporary engineering systems increasingly rely on the coordinated use of heterogeneous modelling paradigms. Data-driven learning models, physics-based representations, optimisation routines, control structures, simulation frameworks, and decision mechanisms are often combined to address structural complexity, operational variability, and performance constraints. JHMIES provides a forum for research that examines how such hybrid modelling configurations are formulated, integrated, and evaluated when embedded in engineering systems.

The journal does not prioritise isolated algorithmic refinement or purely theoretical modelling detached from an engineering context. Instead, it addresses the structural and operational implications of combining distinct modelling paradigms within engineered environments. Questions concerning model coordination, integration logic, system architecture, robustness, scalability, and performance under practical constraints are central to its scope.

Particular attention is given to studies that demonstrate how modelling structure influences measurable engineering outcomes, including system stability, reliability, resource efficiency, and operational behaviour under realistic conditions. Contributions are expected to clearly articulate integration strategies, modelling assumptions, and validation procedures, supported by analytical, computational, or experimental evidence.

JHMIES welcomes interdisciplinary work situated at the intersection of computational modelling, systems engineering, control engineering, industrial engineering, and applied optimisation, provided that the central contribution concerns the structured integration of modelling components within engineering systems.

The journal is published quarterly and follows a standard peer-review procedure to ensure methodological rigour and technical soundness.

Key features of JHMIES include:

• The journal centres on hybrid modelling within engineering systems rather than isolated algorithmic development.

• Emphasis is placed on structural integration, system architecture, and the coordination of heterogeneous modelling components.

• Contributions must demonstrate clear engineering relevance, linking modelling design to measurable system performance.

• Methodological transparency is required, including explicit modelling assumptions, integration logic, and validation procedures.

• Comparative or cross-domain studies are encouraged where they clarify structural differences between alternative modelling configurations.

• Application-oriented studies are considered only when they contribute to modelling methodology or integration structure; descriptive case reports without structural contribution fall outside the journal’s focus.

Scope

JHMIES welcomes original research articles, methodological analyses, theoretical studies, systematic reviews, and well-documented empirical or computational investigations in areas including, but not limited to, the following:

Hybrid Modelling Methodologies

Research addressing the structured combination of heterogeneous modelling paradigms within unified engineering frameworks, including:

• Integration of data-driven and physics-based models

• Coupling of learning, optimisation, and control mechanisms

• Multi-model and ensemble modelling strategies

• Model fusion and hierarchical modelling structures

• Hybrid predictive–prescriptive modelling approaches

• Coordination of deterministic and stochastic representations

Submissions in this area should emphasise modelling structure and integration coherence rather than isolated algorithmic enhancement.

System Architecture and Engineering Integration

Research examining how hybrid models are embedded within broader engineering systems, including:

• System-level modelling and coordination strategies

• Hybrid architectures in cyber-physical environments

• Integration within simulation and digital twin frameworks

• Embedded and real-time modelling systems

• Component interoperability and modular system design

• Interaction between modelling components and physical processes

Studies should demonstrate how modelling integration influences system behaviour and operational performance.

Performance, Robustness, and Operational Behaviour

Research concerned with how hybrid modelling systems perform under engineering constraints, including:

• Operation under uncertainty, noise, and incomplete data

• Stability and robustness analysis

• Sensitivity studies and structured performance evaluation

• Scalability and computational efficiency considerations

• Reliability analysis in integrated modelling systems

• Behaviour under dynamic or time-varying conditions

Emphasis is placed on systematic evaluation rather than anecdotal reporting.

Control, Optimisation, and Decision Structures

Research involving hybrid modelling in control and optimisation contexts, including:

• Hybrid model-based control strategies

• Learning-assisted optimisation frameworks

• Adaptive and multi-objective optimisation

• Predictive modelling integrated with control structures

• Real-time decision-support modelling systems

Submissions should explain how modelling integration improves system structure or operational performance.

Validation, Comparative Studies, and Reproducibility

Research addressing the evaluation and validation of hybrid modelling systems, including:

• Experimental validation of integrated modelling frameworks

• Cross-domain comparative studies

• Integration testing in simulated or operational environments

• Reproducibility and transparency in hybrid modelling research

• Lifecycle performance assessment

Studies should provide clearly defined evaluation criteria and methodological transparency.

Engineering Application Contexts

Hybrid modelling approaches applied within engineering domains such as:

• Manufacturing and industrial systems

• Energy production and power systems

• Robotics and automation

• Transportation and infrastructure systems

• Aerospace and mechanical engineering

• Environmental and sustainability engineering

• Industrial cyber-physical systems

Application-based submissions must retain a clear modelling or integration-oriented contribution rather than presenting domain-specific case descriptions alone.