10C: Data-driven Optimization | AIChE

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10C: Data-driven Optimization

Chair(s)

del Rio Chanona, A., Imperial College London

Co-chair(s)

Yuan, Z., Tsinghua University
Wu, Z., University of California Los Angeles
Yin, X., Nanyang Technological University

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.

Presentations

3:30 PM
(712a) Efficient Local Multi-Fidelity Optimization of High-Dimensional Objective Functions Using Cost-Aware Gradient Entropy Search (CAGES)
3:51 PM
(712b) Gradient Information Bayesian Optimization for Composite Functions (GIBO-CF): Local Physics-Based Grey-Box Optimization in High Dimensions
4:12 PM
(712c) Physics-Informed Neural Networks with Hard Linear Equality Constraints
4:33 PM
(712d) Multistep Lookahead Bayesian Optimization for High Dimensional Black-Box Optimization Problems Using Reinforcement Learning
4:54 PM
(712e) BO4IO: A Bayesian Optimization Approach to Inverse Optimization with Uncertainty Quantification
5:15 PM
(712f) Embedding Data-Driven Classifier Surrogates in Solvent Mixture Design Optimisation Problems
5:36 PM
(712g) Removing the Human from Human-in-the-Loop Bayesian Optimization Via Multimodal Large Language Models, or There and Back Again

Topics 

Computing and Systems Engineering

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