State-of-the-art systems engineering is model-based: models are used in all phases of systems’ lifecycle. An exciting new research direction focuses on symbiotic design where human-driven model-based design processes are complemented by AI/ML assisted components. Our research covers a broad range of engineering activities where models and data are used both in design and in operations.

Cyber-physical systems and human cyber-physical systems where humans and computing are tightly integrated into a physical environment
Design-space exploration, both parametric and combinatorial, with optimization and trade-offs
Fault diagnostics and prognostics, system health management
Foundations for Model-integrated Computing / Model-driven design: meta-programmable modeling tools, formal frameworks, domain-specific modeling languages, model transformations, and run-time environments for model-driven system development
Model integration platforms for physical and biological systems
Resilient systems that can recover from faults of cyber-effects and continue operating
Assurance of Cyber-physical systems with learning-enabled components
Software engineering environments for agile and adaptive system development
System verification and validation, including both formal and coverage-driven methods
Large-scale heterogeneous simulation environments for studying complex, emerging behaviors in system-of-systems

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Future Airborne Capability Environment (FACE) Support (2023)

The FACE(TM) Standard is an Open Architecture approach to creating reusable, interoperable software for DoD Platforms (https://www.opengroup.org/face). The FACE Consortium was formed in 2010 to define an open avionics environment for all military airborne platform types. Vanderbilt has been an Academia team member supporting the FACE standard development effort since its inception in 2009 assisting in the formative concepts and continues to provide key technical guidance in many areas of the FACE Consortium.

CPS: TTP Option: Medium: Collaborative Research: Smoothing Traffic via Energy-efficient Autonomous Driving (STEAD)

Studies show five of the top 10 most-gridlocked cities in the world are in the United States. Traffic congestion puts undue burden on transportation systems across the United States, raising transportation costs and the energy footprint. Vehicle automation creates an opportunity to reduce traffic and improve efficiency of the transportation infrastructure.

Collaborative Research: CPS: TTP Option: Medium: Coordinating Actors via Learning for Lagrangian Systems (CALLS)

This project will improve the ability to build artificial intelligence algorithms for Cyber-Physical Systems (CPS) that incorporate communications technologies by developing methods of learning from simulation environments. The specific application area is connected and automated vehicles (CAV) that drive strategically to reduce stop-and-go traffic.

CIRCLES: Congestion Impact Reduction via CAV-in-the-loop Lagrangian Energy Smoothing

The CIRCLES Website https://circles-consortium.github.io contains more detailed information on this project.

Radiation Effects and Reliability for Space Environments

Understandable and Reusable Formal Verification for Cyber-Physical Systems

This project entitled "Verification of Autonomous Systems" sponsored by the Air Force Office of Scientific Research, is looking at ways to formally verify that systems that may operate on their own or autonomously meet their requirements.

PIRE: Science of Design for Societal-Scale Cyber-Physical Systems

This project aims to develop a new Science of Design for societal-scale Cyber- Physical Systems (CPS).

SHF: Small: Collaborative Research: Fuzzing Cyber-Physical System Development Tool Chains with Deep Learning (DeepFuzz-CPS)

Developing a modern technical product such as a car, plane, or a complex medical device includes designing the complex interplay between sensors (which measure physical product and environment state) and actuators (such as small electric motors that control the product).

FMitF: Track II: Hybrid and Dynamical Systems Verification on the CPS-VO

This project aims to transition recent research results that automate portions of the verification process of Cyber-Physical Systems into broader practice, particularly with industrial and student users. Cyber-physical systems (CPS) are networked embedded computing systems coupled with physics, such as in motor vehicles, aircraft, medical devices, and the electrical grid.

Collaborative Research: Operator theoretic methods for identification and verification of dynamical systems

Widespread use of automation in many sectors of society has yielded a large amount of data regarding historical behaviors for a variety of dynamical systems, such as unmanned aerial, marine, and ground vehicles, biological systems, and weather systems. This project aims to develop novel algorithms to discover governing rules that explain the observed behaviors (i.e., trajectories) of dynamical systems. Discovery of underlying models, while useful for analysis and control, can be computationally challenging.

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