The Systems Science Program conducts interdisciplinary research on the theory and computational use of modeling and simulation methods. These methods draw upon advances in mathematics and the natural sciences, and apply also to engineering, business, and the social sciences. Of particular interest are:
- Complex Adaptive Systems, including learning and evolutionary adaptation, and the analysis of complex structures and networks.
- Computational Intelligence, including neural networks, fuzzy systems, and genetic algorithms for control, pattern recognition, and other AI problems
- Computer modeling and simulation
- Data Mining, including reconstructability analysis, log-linear methods, machine learning, and other approaches to knowledge discovery and representation
- Decision Making, including game and decision theories, risk assessment, multiple perspectives, and policy analysis
- Dynamical Systems, including chaotic dynamics, discrete and continuous system simulation, and time series analysis and forecasting
- Optimization, including critic-based reinforcement-learning approach to implementing approximate dynamic programming, and evolutionary computation
- Signal Processing, including compression, detection, and prediction
Recent applications include medical phenomena (elevated intracranial pressure and autoimmune system disorders), analysis of genomic data, learning-based design of authonomous terrestrial vehicle steering control, and of nonlinear flight control for a hypersonic aircraft, and of context-discernment and experience-based control by/for robots, climate modeling, health care systems, human perception, linguistics, macromolecular structure, criminal justice systems, software development processes, sustainable fisheries, manufacturing processes, medical testing and outcomes analysis, organizational development, product creation and marketing, speech processing, forecasting financial measures, and supply chain logistics.