Data-Driven Dynamic Modeling, Estimation and Control I | AIChE

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Data-Driven Dynamic Modeling, Estimation and Control I

Chair(s)

Kwon, J., Texas A&M University

Co-chair(s)

Tsay, C., Imperial College London

This session highlights techniques that use data to conduct tasks such as dynamic system modeling, 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.

Presentations

3:30 PM
(12a) Active Deep Learning for Scalable Approximation of Reachable and Invariant Sets for Mixed-Integer Nonlinear Systems
3:49 PM
(12b) Development of Mass and Energy Constrained Neural Networks
4:08 PM
(12c) Data-Driven Controller Synthesis through the Learning of Integral Quadratic Constraints
4:27 PM
(12d) Nonlinear Model Predictive Control Using Statistical Machine-Learning-Based Control Lyapunov Barrier Functions
4:46 PM
(12e) Efficient Training of Machine-Learning Enhanced Building Models with Noisy Data
5:05 PM
(12f) A Deep Learning-Based Model Reduction and Control of an Ammonia Synthesis Process
5:24 PM
(12g) State Space Model Predictive Control Using Quality Variables for Batch Polymethyl Methacrylate Production
5:43 PM
(12h) Latent State Space Modeling of High-Dimensional Data in Chemical Systems

Topics 

Process Automation & Control

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Pricing

Individuals

AIChE Pro Members $150.00
AIChE Emeritus Members $105.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
AIChE Explorer Members $225.00
Non-Members $225.00

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