Data Driven Modeling and Decision Making

Ydstie, B. E., Carnegie Mellon University
Nicholson, B., Center for Computing Research, Sandia National Laboratories

Rapid advances in cloud computing, data management, and machine learning has led to new opportunities to integrate systems, computation and decision-making in real time. Initiatives such as Industrie 4.0 and Smart Manufacturing encourage industry, government and academia to work together to advance theory and application. Papers are solicited that describe all aspects of data-driven modeling and decision making as they relate to the chemical manufacturing industries. Industrial contributions and reviews are encouraged. We are interested in theoretical advances in machine learning, applications of learning and related data analysis methods useful for data-driven modeling and decision making in systems with integrated continuous and discrete transitions.



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