299 Data-Driven Techniques for Dynamic Modeling, Estimation and Control I

Monday, November 16, 2020: 8:00 AM - 9:00 AM
Computing and Systems Technology Division (10) (PreRecorded+)

Description:
The session welcomes contributions that develop and apply data-based techniques to conduct system's tasks such as dynamic model development, state/parameter estimation, identification, and control. Emerging techniques from data science and machine learning as well as approaches that combine data with first-principles models are encouraged.

Sponsor:
Systems and Process Control
Co-Sponsor(s):
Information Management and Intelligent Systems (10E)
Chair:
Zhenyu Wang Email: zwang12@dow.com
Co-Chair:
Joseph Sang-Il Kwon Email: kwonx075@tamu.edu


(299a) Mechanistically Inspired Data-Driven COVID-19 Pandemic Modeling for Multiple Countries
S. Joe Qin, Qingpeng Zhang, Fengshi Jing, Fiona Guo and Jerry Li


(299c) Deep Learning Based Koopman System Identification of Nonlinear Controlled Systems
Abhinav Narasingam, Mohammed Saad Faizan Bangi and Joseph Sang-Il Kwon


(299d) Machine Learning-Based Model Predictive Control of Nonlinear Processes Using Noisy Data
Zhe Wu, David Rincon, Zhihao Zhang and Panagiotis D. Christofides


(299e) Disturbance Modeling for Model Predictive Control Using Neural Networks
Pratyush Kumar, James B. Rawlings and Stephen J. Wright


(299f) Fast Approximate Multistage NMPC with Online Scenario Tree Generation Using Active Deep Learning
Joel Paulson, Angelo D. Bonzanini, Georgios Makrygiorgos and Ali Mesbah


(299g) The Integration of Relu-Based Deep Neural Networks for Explicit Model Predictive Control
Iosif Pappas, Justin Katz, Styliani Avraamidou and Efstratios N. Pistikopoulos


(299h) PDE+Pinn: Neural Identification and Solution of Partial Differential Equations on Partial Data
Tom S. Bertalan, Felix Kemeth, Tianqi Cui and Ioannis G. Kevrekidis
See more of this Group/Topical: Computing and Systems Technology Division
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