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

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

Chair(s)

Kwon, J., Texas A&M University

Co-chair(s)

Zhang, Q., University of Minnesota

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

8:00 AM
(246a) Model Predictive Control of Vagus Nerve Stimulation in the Rat Cardiac System Using Long Short-Term Memory Network
8:19 AM
(246b) High Throughput Characterization of Membrane Transport Properties through Data Analytics
8:38 AM
(246c) Pathologies of Neural Networks As Models of Discrete-Time Dynamical Systems
8:57 AM
(246d) Data-Driven Dynamic Latent Variable Analysis of Chaotic and Oscillatory Chemical Systems
9:16 AM
(246e) Online Monitoring Based on Reaction Network and Kinetic Model Identification from Spectroscopic Data without a Priori knowledge of Species and Reactions
9:35 AM
(246f) Emergent Data-Driven Model Reductions for Coupled, Heterogeneous Agent-Based Systems
9:54 AM
(246g) Dynamic Operability Analysis Employing Kriging-Based Surrogate Models
10:13 AM
(246h) Functional Observers for Discrete-Time Nonlinear Systems with Applications to Fault Detection and Estimation

Topics 

Process Automation & Control

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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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