2021 Annual Meeting

Session: Advances in Machine Learning and Intelligent Systems I

Chair

Kwon, J., Texas A&M University

Co-Chair

Presentations

12:30 PM

(104a) Design of Flow Chemistry Experiments Using Batch Bayesian Optimization

12:45 PM

(104b) Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow

01:00 PM

(104c) Systematic Selection of Surrogate Modeling Techniques for Surrogate-Based Optimization Using Presto (Predictive REcommendations of Surrogate models To Optimize)

01:15 PM

(104d) Identification of Critical Process Parameters from Observational Data, a Causal Inference Perspective

01:30 PM

(104e) Efficient Optimization and Accurate Approximation Using Surrogate Models – Tools and Case Studies from RAPID Synopsis Project

01:45 PM

(104f) Data-Driven Tight Underestimation: Application to Process Safety

02:00 PM

(104g) Automated Dimension Reduction with Principal Component Analysis Using Area Under Curve

02:15 PM

(104h) Families of Data-Driven Surrogates Based on Accuracy and Complexity