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Stochastic maximal Lp-regularity for non-autonomous evolution equations with fractional derivative in UMD spaces

Abstract

This paper is concerned with the maximal regularity theory for non-autonomous stochastic evolution equations with a generalized fractional derivative in UMD spaces. The generalized time-fractional derivative provides a unified framework covering both the classical Riemann-Liouville and Caputo fractional derivatives, which accommodates a wider class of anomalous diffusion processes with intermediate memory effects. Based on the singularities of the initial term and the stochastic convolution kernel, a time-weighted space and a regular-singular decomposition are used to obtain the well-posedness, space-time regularity, and stochastic maximal Lp-regularity results. Our results are applied to non-autonomous stochastic diffusion equation and stochastic fractional reaction-diffusion SIR model.

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BibTeX
@misc{tao2026stochastic,
  title = {{Stochastic maximal $L^p$-regularity for non-autonomous evolution equations with fractional derivative in UMD spaces}},
  author = {Lu Lu Tao and Jia Wei He},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.32417},
  eprint = {2609.32417},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.32417},
}
RIS
TY  - UNPB
TI  - Stochastic maximal L^p-regularity for non-autonomous evolution equations with fractional derivative in UMD spaces
AU  - Lu Lu Tao
AU  - Jia Wei He
PY  - 2026
DA  - 2026/09/26
PB  - arXiv
DO  - 10.48550/arxiv.2609.32417
UR  - https://arxiv.org/abs/2609.32417
AB  - This paper is concerned with the maximal regularity theory for non-autonomous stochastic evolution equations with a generalized fractional derivative in UMD spaces. The generalized time-fractional derivative provides a unified framework covering both the classical Riemann-Liouville and Caputo fractional derivatives, which accommodates a wider class of anomalous diffusion processes with intermediate memory effects. Based on the singularities of the initial term and the stochastic convolution kernel, a time-weighted space and a regular-singular decomposition are used to obtain the well-posedness, space-time regularity, and stochastic maximal L^p-regularity results. Our results are applied to non-autonomous stochastic diffusion equation and stochastic fractional reaction-diffusion SIR model.
ER  -