Bibliographic Details
| Title: |
Globally optimized dynamic mode decomposition: A first study in particulate systems modelling |
| Authors: |
Abhishek Gupta, Barada Kanta Mishra |
| Source: |
Theoretical and Applied Mechanics Letters, Vol 15, Iss 1, Pp 100563- (2025) |
| Publisher Information: |
Elsevier, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Engineering (General). Civil engineering (General) |
| Subject Terms: |
Physics-informed dynamic mode decomposition, Population balance equation, Particle breakage, Zeroth-order optimization, Engineering (General). Civil engineering (General), TA1-2040 |
| More Details: |
This paper introduces dynamic mode decomposition (DMD) as a novel approach to model the breakage kinetics of particulate systems. DMD provides a data-driven framework to identify a best-fit linear dynamics model from a sequence of system measurement snapshots, bypassing the nontrivial task of determining appropriate mathematical forms for the breakage kernel functions. A key innovation of our method is the instilling of physics-informed constraints into the DMD eigenmodes and eigenvalues, ensuring they adhere to the physical structure of particle breakage processes even under sparse measurement data. The integration of eigen-constraints is computationally aided by a zeroth-order global optimizer for solving the nonlinear, nonconvex optimization problem that elicits system dynamics from data. Our method is evaluated against the state-of-the-art optimized DMD algorithm using both generated data and real-world data of a batch grinding mill, showcasing over an order of magnitude lower prediction errors in data reconstruction and forecasting. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2095-0349 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2095034924000746; https://doaj.org/toc/2095-0349 |
| DOI: |
10.1016/j.taml.2024.100563 |
| Access URL: |
https://doaj.org/article/f9084fa38f3a48f2bdb6eb1814ce8e1f |
| Accession Number: |
edsdoj.f9084fa38f3a48f2bdb6eb1814ce8e1f |
| Database: |
Directory of Open Access Journals |
