EulerSolver#

class deepinv.sampling.EulerSolver(timesteps, rng=None)[source]#

Bases: BaseSDESolver

Euler-Maruyama solver for SDEs.

This solver uses the Euler-Maruyama method to numerically integrate SDEs. It is a first-order method that approximates the solution using the following update rule:

\[x_{t+dt} = x_t + f(x_t,t)dt + g(t) W_{dt}\]

where \(W_t\) is a Gaussian random variable with mean 0 and variance dt.

Parameters:

  • timesteps (torch.Tensor) – The time steps at which to evaluate the solution.

  • rng (torch.Generator) – A random number generator for reproducibility.

Examples using EulerSolver:#

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs