Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
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Abstract (arXiv)
In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework, to demonstrate its potential. The numerical results show the ability of the reduced-order models constructed with operator inference to provide a reduced yet accurate surrogate solution. This represents an important milestone towards the implementation of fast and reliable digital twin architectures.
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References · 12
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
- 2019Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemscited by 96
- 2020Data-driven reduced-order models via regularized operator inference for a single-injector combustion processcited by 42
- 2021Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approachcited by 36
- 2020Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear termscited by 32
- 2021Non-intrusive reduced-order models for parametric partial differential equations via data-driven operator inferencecited by 23
- 2019Sampling low-dimensional Markovian dynamics for pre-asymptotically recovering reduced models from data with operator inferencecited by 20
- 2021Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inferencecited by 16
- 2020Operator Inference and Physics-Informed Learning of Low-Dimensional Models for Incompressible Flowscited by 10
- 2023Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inferencecited by 10
- 2021Active operator inference for learning low-dimensional dynamical-system models from noisy datacited by 5
- 2021LQResNet: A Deep Neural Network Architecture for Learning Dynamic Processescited by 4
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