2021 Annual Meeting

Session: Advances in Machine Learning and Intelligent Systems I

Chair

Joseph Kwon, Texas A&M University

Co-Chair

Presentations

12:30 PM

(104a) Design of Flow Chemistry Experiments Using Batch Bayesian Optimization
Calvin Tsay, Jose Pablo Folch, Mark van der Wilk, Behrang Shafei, David Walz, Astrid Niederle, Ruth Misener

12:45 PM

(104b) Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow
Jordan Jalving, Michael Eydenberg, Logan Blakely, Zachary Kilwein, Fani Boukouvala, Carl Laird

01:00 PM

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

01:15 PM

(104d) Identification of Critical Process Parameters from Observational Data, a Causal Inference Perspective
Shu Yang, B. Wayne Bequette

01:30 PM

(104e) Efficient Optimization and Accurate Approximation Using Surrogate Models – Tools and Case Studies from RAPID Synopsis Project
Bianca Williams, Sun Hye Kim, Mohammed Sadaf Monjur, Suryateja Ravutla, Fani Boukouvala, Selen Cremaschi, Faruque Hasan, Simon Leyland, Joannah Otashu

01:45 PM

(104f) Data-Driven Tight Underestimation: Application to Process Safety
Ahmed Harhara, Faruque Hasan, Chinmay Aras

02:00 PM

(104g) Automated Dimension Reduction with Principal Component Analysis Using Area Under Curve
Monique McClain, Kris Villez, Dhrubajit Chowdhury

02:15 PM

(104h) Families of Data-Driven Surrogates Based on Accuracy and Complexity
Maaz Ahmad, Iftekar Karimi