Machine Learning, Control and Optimization for Systems and Processes
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".
Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 1072
Special Issue Editor
Special Issue Information
Dear Colleagues,
Novel machine learning algorithms are now being used in combination with physics-based modelling in engineering to tackle traditionally intractable problems. Many developments are appearing in this field with multiple researchers addressing the physics informed machine learning question in different applications. We propose a Special Issue of the journal Mathematics, focusing on the use of machine learning tools for the simulation, optimization, and control of real-time industrial processes. This Special Issue aims to collect the recent advances and developments in the models addressing physics-based machine learning techniques and applications related to the industrial systems and processes, especially dynamic applications requiring a fast and reliable feedback, ultimately in real-time. These dynamic applications involve an additional layer of complexity when creating an integrator, which is a simulator not accessing the exact outputs of the physical system at every time-step. Integrators aim to forecast an application response in the far future, after multiple time-steps.
Prof. Dr. Chady Ghnatios
Guest Editor
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Keywords
- physics-based machine learning
- simulation and optimization
- control of dynamic systems
- integrator systems
- dynamic system forecast
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