Sampling from Neural Operator Priors to Solve Partial Differential Equations
The authors combined pseudo-bootstrap sequential Monte Carlo (pBS) sampling with a U-shaped Neural Operator (UNO) diffusion prior to solve partial differential equations in function space. They adapted a diffusion solver to incorporate guidance terms, using pBS to maintain particle diversity and improve accuracy across various benchmarks, showing superior error-versus-time performance.