Achieving robustness in hybrid models: A physics-informed regularization approach for spatiotemporal parameter estimation in PDEs-Reference-Cited by-同舟云学术

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Achieving robustness in hybrid models: A physics-informed regularization approach for spatiotemporal parameter estimation in PDEs

Published:2024-04 Issue: Volume:204 Page:292-302
ISSN:0263-8762
Container-title:Chemical Engineering Research and Design
language:en
Short-container-title:Chemical Engineering Research and Design

Author:

Pahari Silabrata,Shah Parth^ORCID,Kwon Joseph Sang-Il^ORCID

Publisher

Elsevier BV

Reference80 articles.

1. Data-based modeling and control of nonlinear process systems using sparse identification: An overview of recent results;Abdullah;Comput. Chem. Eng.,2023

2. Gray-box modeling for the optimization of chemical processes;Asprion;Chem. Ing. Tech.,2019

3. Parameter Estimation and Inverse Problems;Aster,2018

4. A generic hybrid model development for process analysis of industrial fixed-bed catalytic reactors;Azarpour;Chem. Eng. Res. Des.,2017

5. Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae;Bangi;Chem. Eng. Res. Des.,2022

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1. Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes;Computers & Chemical Engineering;2024-12

2. Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization;Computers & Chemical Engineering;2024-11

3. Leveraging neural networks to estimate parameters with confidence intervals;The Canadian Journal of Chemical Engineering;2024-08-07

4. Empowering Hybrid Models with Attention-Based Time-series Transformers: A Case Study in Batch Crystallization;2024 American Control Conference (ACC);2024-07-10

5. A Hybrid Modeling Framework for Catalytic Systems: Sensitivity Analysis and Estimation of Activation Energies;2024 American Control Conference (ACC);2024-07-10

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