  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.
