An Inverse Source Problem for Semilinear Stochastic Hyperbolic Equations

This paper investigates an inverse source problem for general semilinear stochastic hyperbolic equations. Motivated by the challenges arising from both randomness and nonlinearity, we develop a globally convergent iterative regularization method that combines Carleman estimate with fixed-point iteration. Our approach enables the reconstruction of the unknown source function from partial lateral Cauchy data, without requiring a good initial guess. We establish a new Carleman estimate for stochastic hyperbolic equations and prove the convergence of the proposed method in weighted spaces. Furthermore, we design an efficient numerical algorithm that avoids solving backward stochastic partial differential equations and is robust to randomness in both the model and the data. Numerical experiments are provided to demonstrate the effectiveness of the method.