Machine-learning invariant foliations in forced systems for reduced order modelling
<|reference_start|>Machine-learning invariant foliations in forced systems for reduced order modelling: We identify reduced order models (ROM) of forced systems from data using invariant foliations. The forcing can be external, parametric, periodic or quasi-periodic. The process has four steps: 1. identify an approximate invariant torus and the linear dynamics about the torus; 2. identify a globally defined invariant foliation about the torus; 3. identify a local foliation about an invariant manifold that complements the global foliation 4. extract the invariant manifold as the leaf going through the torus and interpret the result. We combine steps 2 and 3, so that we can track the location of the invariant torus and scale the invariance equations appropriately. We highlight some fundamental limitations of invariant manifolds and foliations when fitting them to data, that require further mathematics to resolve.<|reference_end|>
@article{szalai2024machine-learning,
    title={Machine-learning invariant foliations in forced systems for reduced
  order modelling},
    author={Robert Szalai},
    journal={arXiv preprint arXiv:2403.14514},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14514},
    primaryClass={math.DS cs.LG}
}