2020/10/31 by Nicholas Geneva, Nicholas Zabaras · 1 citation
Computer Science · Physics and Astronomy · #cs.LG #physics.comp-ph
paper · pdf · doi:10.1016/j.neunet.2021.11.022
published as Neural Networks; Volume 146, February 2022, Pages 272-289 · 22 pages, 14 figures, 3 appendices
arxiv created 2021/12/13 · arxiv updated 2021/12/15
Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. The use of Koopman based embeddings provide a unique and powerful method for projecting any dynamical system into a vector representation which can then be predicted by a transformer. The proposed model is able to accurately predict various dynamical systems and outperform classical methods that are commonly used in the scientific machine learning literature.