Dynamic operator-guided flow matching: A generative physical inverse solver for arbitrary sparse observations
Solving ill-posed physical inverse problems under extremely sparse observations remains a fundamental engineering challenge. Conventional inversion frameworks require repeated, computationally prohibitive forward simulations. While deep learning-accelerated approaches (e.g., Diffusion Posterior Sampling) mitigate the...