Can I read Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning on EtoBox?

Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning by Farlessyost, William; Singh, Shweta is a scholarly article available to read on EtoBox.

What is Reduced Order Dynamical Models For Complex Dynamics in Manufacturing and Natural Systems Using Machine Learning about?

Dynamical analysis of manufacturing and natural systems provides critical information about production of manufactured and natural resources respectively, thus playing an important role in assessing sustainability of these systems. However, current dynamic models for these systems exist as mechanistic models, simulation of which is computationally intensive and does not provide a simplified understanding of the mechanisms driving the overall dynamics. For such systems, lower-order models can prove useful to enable sustainability analysis through coupled dynamical analysis. There have been few attempts at finding low-order models of manufacturing and natural systems, with existing work focused on model development of individual mechanism level. This work seeks to fill this current gap in the literature of developing simplified dynamical models for these systems by developing reduced-order models using a machine learning (ML) approach. The approach is demonstrated on an entire soybean-oil to soybean-diesel process plant and a lake system. We use a grey-box ML method with a standard nonlinear optimization approach to identify relevant models of governing dynamics as ODEs using the dat

Author

Farlessyost, William; Singh, Shweta

Published

2021

Language

EN