Data-driven and Surrogate Optimization in Operation I | AIChE

Data-driven and Surrogate Optimization in Operation I

Co-chair(s)

Allman, A., University of Michigan
Singh, R., Rutgers, The State University of New Jer

The session invites papers in the general area of data-driven and surrogate based optimization. Topics on theory, algorithms and software for data-driven modeling, digital twins, derivative-free optimization, surrogate modeling and optimization, black-box and grey-box optimization, machine learning/AI-embedded optimization, etc., are of interest. Application papers offering insights into the interplay between data-driven optimization theory and practice are also encouraged.

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AIChE Explorer Members $225.00
Non-Members $225.00