Peer-reviewedCC BY 4.0Mathematics
Recovering the Fractional Order and an Initial Value Simultaneously in a Time-Fractional Diffusion-Wave Equation
- 1School of Mathematics and Statistics, Qinghai Normal University, Xining 810001, China
- 2College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, China
Abstract
For a one-dimensional time-fractional diffusion-wave equation, we investigate joint recovery of its fractional order and the initial velocity from measurements taken at one interior point. Uniqueness is proved through analytic continuation, Laplace transforms, an integral mean value theorem, and Mittag-Leffler function properties. The ill-posed reconstruction is regularized in a Bayesian setting using the iterative regularizing ensemble Kalman method. The posterior measure is shown to exist and to depend stably on the data; its numerical approximation converges under an assumed error bound for the forward solver. The resulting method is assessed in three numerical tests, which illustrate its effectiveness as well as its stability.
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BibTeX
@article{wang2026recovering,
title = {{Recovering the Fractional Order and an Initial Value Simultaneously in a Time-Fractional Diffusion-Wave Equation}},
author = {Kun Wang and Jun Xian and Lei Zhang},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14193641},
url = {https://www.mdpi.com/2227-7390/14/19/3641},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Recovering the Fractional Order and an Initial Value Simultaneously in a Time-Fractional Diffusion-Wave Equation
AU - Kun Wang
AU - Jun Xian
AU - Lei Zhang
PY - 2026
DA - 2026/10/08
JO - Mathematics
DO - 10.3390/math14193641
UR - https://www.mdpi.com/2227-7390/14/19/3641
AB - For a one-dimensional time-fractional diffusion-wave equation, we investigate joint recovery of its fractional order and the initial velocity from measurements taken at one interior point. Uniqueness is proved through analytic continuation, Laplace transforms, an integral mean value theorem, and Mittag-Leffler function properties. The ill-posed reconstruction is regularized in a Bayesian setting using the iterative regularizing ensemble Kalman method. The posterior measure is shown to exist and to depend stably on the data; its numerical approximation converges under an assumed error bound for the forward solver. The resulting method is assessed in three numerical tests, which illustrate its effectiveness as well as its stability.
SN - 2227-7390
ER -