Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors
This addresses the challenge of accurate Bayesian inversion without derivatives for applications where computing derivatives is difficult, representing a strong specific gain in this domain.
The paper tackles the problem of derivative-free Bayesian inversion in science and engineering by introducing Blade, a method that uses diffusion priors and interacting particles, achieving superior performance on various inverse problems like fluid dynamics.
Derivative-free Bayesian inversion is an important task in many science and engineering applications, particularly when computing the forward model derivative is computationally and practically challenging. In this paper, we introduce Blade, which can produce accurate and well-calibrated posteriors for Bayesian inversion using an ensemble of interacting particles. Blade leverages powerful data-driven priors based on diffusion models, and can handle nonlinear forward models that permit only black-box access (i.e., derivative-free). Theoretically, we establish a non-asymptotic convergence analysis to characterize the effects of forward model and prior estimation errors. Empirically, Blade achieves superior performance compared to existing derivative-free Bayesian inversion methods on various inverse problems, including challenging highly nonlinear fluid dynamics.