Systems Engineering

Model-based systems engineering, integration, cyber-physical systems, analysis and control of stochastic systems, Markov decision processes, discrete event and hybrid systems, network optimization and management

ISR is a leader in using model-based systems engineering as an important tool in its research efforts.  Our Master of Science in Systems Engineering degree has given generations of students the advantage of "systems thinking." Our education program is not focused on knowledge in a single core domain, but rather teaches principles and methods applicable across domains as students study integration and design problems that involve multiple engineering disciplines. Students are taught design, analysis and optimization methods not found in other programs.

ISR systems engineering news

Recent ISR systems engineering publications


Architecting for Digital Twins and MCE with AI/ML, Part II

Mark Blackburn, Mark Austin

The final report of research conducted with Stevens Institute of Technology in its Systems Engineering Research Center UARC. The authors investigated digital twin design architectures that support Artificial Intelligence (AI) and Machine Learning (ML) formalisms working side-by-side as a team. The unique aspects of this research are the use semantic technologies that can leverage AI and ML providing complementary and supportive roles in the collection, formalizing representations and processing of data, identification and correlation of events, in evolving spatial contexts and automated decision making throughout the system lifecycle. The research developed graph embedding procedures with ML tasks, which together can enhance digital twin design and decision making to factor in evolving temporal and spatial information, such as those encountered in urban settings.

Final Technical Report SERC-2021-TR-007


Teaching Machines to Understand Urban Networks: A Graph Autoencoder Approach

Maria Coelho, Mark Austin, Shivam Mishra, Mark Blackburn

Due to remarkable advances in computer, communications and sensing technologies over the past three decades,large-scale urban systems are now far more heterogeneous and automated than their predecessors. They may, in fact, be connected to other types of systems in completely new ways. These characteristics make the tasks of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. We believe these challenges can be addressed by teaching machines to understand urban networks. This paper explores opportunities for using a recently developed graph autoencoding approach to encode the structure and associated network attributes as low-dimensional vectors. We exercise the proposed approach on a problem involving identification of leaks in urban water distribution systems.

IARIA International Journal on Advances in Networks and Services

Teaching Machines to Understand Urban Networks

Maria Coelho, Mark Austin

Explores opportunities for using recently developed graph embedding procedures to encode the structure and associated network attributes as low-dimensional vectors. The AI/ML concept is demonstrated on a problem involving identification of leaks in an urban water distribution system.

15th International Conference on Systems (ICONS 2020)

Architecting Smart City Digital Twins: Combined Semantic Model and Machine Learning Approach

Mark Austin, Parastoo Delgoshaei, Maria Coelho, Mohammad Heidarinejad

Explores the approaches and challenges of architecting and operating smart city digital twins, and proposes a path that supports semantic knowledge representation and reasoning, as well as machine learning formalisms, to provide complementary and supportive roles in collecting and processing data, identifying events, and automating decision making.

Journal of Management in Engineering


Using Semantic Fluency Models Improves Network Reconstruction Engineering Knowledge

Thurston Sexton, Mark Fuge

The paper directly models a cognitive process by which technicians may record work orders, recovering implied engineering knowledge about system structure by processing written records.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference

Checking the automated construction of finite element simulations from Dirichlet boundary conditions

Keven Chiu, Mark Fuge

From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions.

ASME 2019 International Design Engineering Technical Conference/Computers and Information in Engineering Conference