融合物理约束的生成式空间声场重构方法

To address the ill-posedness and generalization challenges associated with sound field reconstruction under sparse measurement conditions, this paper proposes a physics-constrained generative reconstruction framework. Specifically, the proposed method employs a plane wave decomposition model to represent the spatial sound field, transforming it into plane wave spectrum coefficients. Utilizing a conditional invertible neural network (CINN) as the backbone, the model learns the conditional posterior probability distribution from sparse observations to spectrum coefficients using large-scale simulation data, effectively modeling the uncertainty inherent in the inverse problem. During the inference phase on real-world data, a fine-tuning mechanism based on Helmholtz equation residuals is introduced as a physical constraint to correct the generated results and enforce physical consistency. Experimental results on the MeshRIR dataset demonstrate that the proposed method significantly outperforms mainstream baselines, including physics-informed neural networks, generative adversarial networks, and the original CINN, in terms of both normalized mean squared error and modal assurance criterion.