Data-Driven and Hybrid Modeling for Decision Making

Chair(s):
Nicholson, B., Center for Computing Research, Sandia National Laboratories
Co-chair(s):
Hasan, M. M. F., Artie McFerrin Department of Chemical Engineering, Texas A&M University

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, or integration of first-principles with data-driven models for decision making. Industrial contributions and reviews are encouraged. We are interested in both theoretical advances and applications in data-driven or hybrid modeling in systems with integrated continuous and discrete transitions.

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Non-Members $225.00