2024 AIChE Annual Meeting
Session: 10C: Data-driven Optimization
The session invites papers in the general area of data-driven and surrogate-based optimization. Topics on theory, algorithms and software for 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.
Chair
Antonio del Rio Chanona, Imperial College London
Co-Chairs
Zhihong Yuan, Tsinghua University
Zhe Wu, University of California Los Angeles
Xunyuan Yin, Nanyang Technological University