Modeling, Simulation and Training for Federal Government Agencies

Background: Federal government agencies are increasingly seeking advanced technologies to enhance their Modeling, Simulation and Training (MST) capabilities through machine learning-driven solutions. The complexity of modern operational environments requires more adaptive, data-driven, and realistic simulation platforms. Integrating machine learning with MST can significantly improve scenario fidelity, accelerate training cycles, and support mission readiness.

Challenges: Agencies face challenges in creating scalable, realistic, and adaptive training environments that can keep pace with evolving threats and operational requirements. Traditional simulation models often lack the ability to learn from new data or adapt to unforeseen scenarios, limiting their effectiveness. Machine learning integration can address these limitations by enabling dynamic content generation and real-time scenario adaptation.

Needs: Patriot Labs is interested in innovative machine learning-enabled solutions that advance Modeling, Simulation and Training capabilities for federal government agencies. There is a critical need for platforms that can autonomously generate, adapt, and optimize training scenarios based on real-world data and user performance. Such innovation will ensure training remains relevant, effective, and responsive to emerging mission demands.

Requirements: Preferred technical solutions may employ or include machine learning algorithms for scenario generation, behavior modeling, and adaptive feedback within MST environments. Solutions should support integration with existing simulation frameworks, leverage large-scale data sources, and provide secure, scalable deployment options. Emphasis should be placed on explainability, interoperability, and compliance with federal cybersecurity standards.

Characteristics: Solutions should provide or enable real-time adaptation to trainee actions, automated scenario complexity adjustment, and continuous learning from operational data. Additional desirable features include intuitive user interfaces, modular architecture for easy updates, and robust analytics for performance assessment. Compatibility with both legacy and next-generation simulation systems is highly valued.

Benefits: Benefits sought include accelerated training effectiveness, improved operational realism, and enhanced decision-making through data-driven insights. Machine learning-driven MST solutions can reduce training costs, shorten readiness timelines, and enable persistent, on-demand training opportunities. These capabilities will support mission success by ensuring personnel are better prepared for complex, dynamic operational environments.

Approaches: Approaches could include developing machine learning models trained on historical mission data to generate realistic simulation scenarios, implementing reinforcement learning for adaptive training modules, and integrating natural language processing for immersive virtual environments. Iterative prototyping, user feedback loops, and phased deployment strategies are recommended to ensure operational relevance and scalability. Collaboration with subject matter experts and continuous validation against real-world outcomes will further enhance solution effectiveness.

Special Consideration: Special consideration given to solutions that include or enable autonomous scenario generation, adaptive learning pathways, and seamless integration with live, virtual, and constructive training environments using advanced machine learning techniques.

Publish Date: 8/9/2026

Capability Focus Area: Modeling, Simulation and Training

Announcement Type: CFI

Applicable Agencies: federal government agencies

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