Verification, Validation, and Uncertainty Quantification for Modeling & Simulation Training by Tonex
Verification, Validation, and Uncertainty Quantification for Modeling & Simulation Training by Tonex is a 2-day professional course designed to help technical teams evaluate model accuracy, establish acceptance criteria, quantify uncertainty, and communicate model credibility for high-consequence decisions. Participants learn practical methods for planning V&V activities, checking algorithms and code, comparing model outputs with test evidence, analyzing uncertainty sources, and documenting findings for engineering and program stakeholders.
Cybersecurity is strongly connected to model trust, especially when analytical tools support mission, defense, aerospace, infrastructure, and safety decisions. Strong cybersecurity practices help protect model data, test evidence, assumptions, and decision records from tampering or unauthorized changes. This course also emphasizes traceability and disciplined reporting so cybersecurity and quality teams can defend model credibility in regulated environments.
Learning Objectives
- Understand core V&V principles for analytical model development and assessment
- Define model requirements, acceptance criteria, and decision thresholds
- Apply structured verification methods for algorithms, code, and calculations
- Compare model outputs against experimental, operational, and field evidence
- Identify uncertainty sources and evaluate their effect on results
- Perform sensitivity analysis to determine key model drivers
- Build credibility evidence that supports engineering and management decisions
- Strengthen cybersecurity awareness by protecting model data, assumptions, validation evidence, and reporting integrity
Audience
- Simulation engineers
- Test engineers
- Systems engineers
- Quality engineers
- V&V teams
- Modeling and analysis professionals
- Reliability engineers
- Program managers
- Technical decision makers
- Cybersecurity Professionals
Course Modules
Module 1: V&V Foundations and Concepts
- Role of verification and validation
- Model lifecycle review points
- Credibility versus accuracy
- Evidence-based decision support
- Stakeholder expectation alignment
- Common V&V failure patterns
Module 2: Requirements and Acceptance Criteria
- Model purpose definition
- Operational use boundaries
- Performance acceptance thresholds
- Traceable requirement mapping
- Risk-based criteria selection
- Decision authority expectations
Module 3: Algorithms and Code Verification
- Numerical method review
- Logic and calculation checks
- Code inspection practices
- Software configuration control
- Error detection techniques
- Regression test planning
Module 4: Experimental Data Validation
- Test data quality review
- Field evidence comparison
- Measurement uncertainty handling
- Data cleaning considerations
- Validation metric selection
- Result agreement interpretation
Module 5: Uncertainty and Sensitivity Methods
- Input uncertainty sources
- Parameter variation effects
- Propagation method selection
- Scenario-based uncertainty review
- Sensitivity ranking approaches
- Confidence interval interpretation
Module 6: Credibility and Reporting
- Credibility matrix development
- Evidence package structure
- Assumption documentation methods
- Decision-maker reporting formats
- Review board preparation
- Audit-ready record management
Strengthen analytical confidence, reduce decision risk, and improve model credibility with Verification, Validation, and Uncertainty Quantification for Modeling & Simulation Training by Tonex.