The PhD project explores the intersection between machine learning and dynamical systems. Throughout the project multiple research
directions are investigated. Firstly, the effect of constraining networks with known dynamical properties is researched. Secondly, the project
explores dynamical forecasting when the underlying dynamical system is unknown, however, one can identify underlying static factors of
variation. Furthermore, the project looks into disentangling and identifying dynamic factors via interventions and causal modeling. Lastly, the
project investigates the concept of dynamics in the context of state-of-the-art machine learning models, such as transformers and diffusion
models.