Risk Modeling, Analysis, and Management in Complex Dynamical Systems
Numerous lives and billions of dollars are lost periodically in industrial accidents as the Bhopal, Piper Alpha, BP Texas City and BP Deepwater Horizon accidents remind us. To design and operate complex industrial processes more safely and optimally, we are developing automation methodologies for the identification, analysis, and control of risks in process systems using real-time hybrid intelligent systems. Professor Venkat and his group have been exploring various approaches for the integration of process monitoring, data reconciliation, fault diagnosis, and supervisory control tasks into a single unified real-time framework. Knowledge-based systems, neural networks, statistical techniques and mathematical programming approaches are being developed to address these problems.
“Big Data” Analytics for Molecular Products Design and Discovery
Designing new materials and formulations with desired properties is an important and difficult problem, encompassing a wide variety of products in the specialty chemicals and pharmaceuticals industries. Traditional trial-and-error design and discovery approaches are laborious and expensive, and cause delays time-to-market as well as miss some potential solutions. Furthermore, the growing avalanche of high throughput experimentation data has created both an opportunity, and a major modeling and informatics challenge, for material design and discovery. A new paradigm is needed that increases the idea flow, broadens the search horizon, and archives the knowledge from today’s successes to accelerate those of tomorrow.
"Big Data" will play a crucial role in molecular products design, process development and commercial scale manufacturing by streamlining information gathering, data integration, model development, and managing all these for easy and timely access and reuse. Towards this goal, we have developed a novel framework, called Discovery Informatics, that is based on knowledge-based systems, statistical machine learning, neural networks and genetic algorithms, for the rapid discovery and design of new materials such as fuel additives, rubber compounds, polymers, catalysts, and nanomaterials. We are also developing another related paradigm, called Ontological Informatics, that involves the development and implementation of ontologies and algorithms for knowledge representation and information sharing to support decision-making in pharmaceutical manufacturing.
Complex Adaptive Teleological Systems
An important challenge facing the 21st century science is in understanding how complex adaptive systems composed of millions of relatively simple interacting entities produce complex emergent behavior. Such emergent behavior is seen in a wide range of problems such as the behavior of ant colonies, flocking behavior of birds, investor behavior in stock markets, consciousness in brain and so on.
Our group is interested in the modeling, analysis, control and optimization of such emergent phenomena in complex, adaptive, networked, teleological systems via self organization. Teleological systems are systems with a purpose or goal. They are specifically designed, or evolved, to achieve this purpose in some operating environment, often competitive or even hostile. They are different from ordinary thermodynamical systems, such as gas molecules enclosed in a container, which are governed by the laws of statistical thermodynamics. The molecules themselves are purpose-free.
Complex teleological systems may be human-designed, such as the ones in engineering (e.g., Internet, transportation networks, national power grids, etc.), economics (e.g., corporations, supply chain networks, etc.), sociology (e.g., governmental organizations), and so on. They can also be naturally evolved complex systems such as cellular and metabolic networks, protein interaction networks, food webs, ecosystems, etc. One may view the purpose or goal as a survival objective of teleological systems in some environment. For example, in biology, an organism needs to execute the functions of reproduction and growth, at some desired performance levels dictated by the environment, in order to survive, grow and propagate its species. In economics, a corporation needs to execute a variety of business and/or manufacturing functions efficiently, safely, and quickly in order to survive and grow in a competitive market for its customers. Professor Venkat's group is exploring a deeper understanding of such networks using statistical mechanics, optimization, artificial intelligence, and artificial life models.