Corpus ID: 232417452

Randomised one-step time integration methods for deterministic operator differential equations

@article{Lie2021RandomisedOT,
  title={Randomised one-step time integration methods for deterministic operator differential equations},
  author={H. Lie and M. Stahn and T. Sullivan},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.16506}
}
Uncertainty quantification plays an important role in applications that involve simulating ensembles of trajectories of dynamical systems. Conrad et al. (Stat. Comput., 2017) proposed randomisation of deterministic time integration methods as a strategy for quantifying uncertainty due to time discretisation. We consider this strategy for systems that are described by deterministic, possibly non-autonomous operator differential equations defined on a Banach space or a Gelfand triple. We prove… Expand

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