融合物理约束的生成式空间声场重构方法OA

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 pro-posed 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 condi-tional posterior probability distribution from sparse observations to spectrum coefficients using large-scale simulation data,effec-tively 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 en-force physical consistency.Experimental results on the MeshRIR dataset demonstrate that the proposed method significantly out-performs 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.