Journal of Model Based Research invites high quality submissions in model based research, computational simulation, and data driven modeling across scientific and engineering disciplines. We welcome original research, reviews, and methods papers that demonstrate validated models, clear assumptions, and reproducible outcomes.

Our editorial priorities focus on rigor, transparency, and real world impact. Submissions should explain how models support decision making, optimization, prediction, or mechanistic understanding.

Submissions that connect modeling insights to real world decisions are prioritized, especially those that compare alternatives, quantify uncertainty, or demonstrate operational impact. Interdisciplinary work is welcome when the modeling contribution is explicit and validation supports practical use.

Manuscripts should communicate the practical implications of results and, where possible, compare model outcomes to established baselines or operational benchmarks in policy planning contexts. Clear figures and summaries that translate model outputs for non specialist stakeholders are highly valued.

• Model based systems engineering and digital twin development
• Multiscale simulation, uncertainty quantification, and sensitivity analysis
• Optimization, control systems, and decision support modeling
• Computational mechanics, fluid dynamics, and structural modeling
• Data driven modeling, machine learning, and hybrid physics informed approaches
• Health, energy, climate, and industrial modeling applications
• Verification, validation, and calibration of complex models
• Open source modeling tools and reproducible workflows
• Explainable modeling and interpretable simulation outputs for stakeholders
• Model governance, risk, and assurance for high impact decisions
• Integration of experimental data, lab automation, and model updating

• Short communications reporting high impact modeling insights
• Technical notes on model calibration, error analysis, or verification
• Perspective articles on modeling standards and best practices

Successful submissions present a clear research question, well defined model structure, and complete documentation of data sources, assumptions, and parameters. We value models that can be replicated and reused by the broader research community.

Authors should highlight novelty, explain why the model improves current approaches, and provide validation results aligned with the intended use case.

Provide a concise model summary that describes inputs, outputs, and key assumptions so reviewers can understand the structure before diving into equations. Explain data provenance, decision context, and how the model supports interpretation or action.

• Transparent model assumptions and boundary conditions
• Validation against experimental or benchmark data
• Sensitivity and uncertainty analysis where appropriate
• Clear interpretation of model outcomes and limitations
• Model diagram or workflow schematic that maps inputs to outputs
• Computational resources, solver settings, and runtime notes when relevant
• Benchmarking against baselines with stated evaluation criteria
• Data and code availability details with access conditions

JMBR values reproducibility across platforms and research contexts. Provide sufficient detail for independent replication, including preprocessing steps, parameter sources, and versioned software or libraries. If code or data are restricted, explain access pathways and justify constraints.

For stochastic models, report how randomness is handled and the number of runs or seeds. For calibrated models, distinguish calibration from validation data and describe goodness of fit metrics. Clear reporting improves review efficiency and confidence in outcomes.

Authors should document calibration protocols, goodness of fit thresholds, and how calibration differs from validation datasets. When results depend on scenario assumptions, define the scenario boundaries and justify parameter ranges to reduce ambiguity in interpretation.

• Data availability statement with repository links or qualified access instructions
• Preprocessing or feature engineering steps applied to input data
• Sensitivity analysis or uncertainty quantification summary
• Validation datasets and benchmark comparisons tied to model objectives
• Notation and acronyms defined consistently across the manuscript
• Ethics or compliance notes for sensitive, clinical, or proprietary data
• Digital twin synchronization frequency and update criteria when applicable
• Limitations and appropriate use cases for decision support
• Calibration criteria and goodness of fit metrics reported with thresholds
• Scenario assumptions and parameter ranges stated with rationale

Submissions undergo editorial screening for scope fit, data transparency, and model documentation. Qualified manuscripts move to single blind peer review with subject matter experts.

We aim to provide clear reviewer feedback that supports revision and reproducibility. Authors may be asked to clarify assumptions, add validation results, or expand data and code access statements to improve transparency and reuse.

JMBR operates under an open access model to ensure modeling research is discoverable and reusable. APCs are applied after acceptance and support peer review, production, and archiving services.

Membership options and affordable APC waivers are available for eligible authors. Contact the editorial office at [email protected] for guidance.

If you require a fee quote for institutional approvals or funder reporting, the editorial office can provide itemized APC documentation after initial screening.

• Scope fit confirmed for model based research
• Model assumptions and parameters documented
• Data availability statement included
• Validation results and evaluation metrics reported
• Cover letter explains novelty and application impact

JMBR is committed to rigorous, transparent publishing in model based research. We emphasize reproducible methods, complete data statements, and ethical compliance across all article types.

The editorial office supports authors, editors, and reviewers with clear guidance and responsive communication. For questions about scope or workflow, contact [email protected].

We encourage continuous improvement in reporting practices and share updates that help the community maintain high standards in computational and simulation research.