Numerical Ergodicity of Stochastic Allen--Cahn Equation driven by Multiplicative White Noise
<|reference_start|>Numerical Ergodicity of Stochastic Allen--Cahn Equation driven by Multiplicative White Noise: We establish the unique ergodicity of a fully discrete scheme for monotone SPDEs with polynomial growth drift and bounded diffusion coefficients driven by multiplicative white noise. The main ingredient of our method depends on the satisfaction of a Lyapunov condition followed by a uniform moments' estimate, combined with the irreducibility and the strong Feller property for full discretization. We transform the original stochastic equation into an equivalent random equation where the discrete stochastic convolutions are uniformly controlled to derive the desired uniform moments' estimate. Applying the main result to the stochastic Allen--Cahn equation driven by multiplicative white noise indicates that this full discretization is uniquely ergodic for any interface thickness. Numerical experiments validate our theoretical results.<|reference_end|>
@article{liu2024numerical,
    title={Numerical Ergodicity of Stochastic Allen--Cahn Equation driven by
  Multiplicative White Noise},
    author={Zhihui Liu},
    journal={arXiv preprint arXiv:2408.02935},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02935},
    primaryClass={math.NA cs.NA math.PR}
}