Sampling with Constraints

Doublewell Nonequilibrium Steady State

For the double well potential, $V(x) = (x^2-1)^2$, consider the overdamped Langevin dynamics:

\[dX_t = - \nabla V(X_t)dt + \sqrt{2\beta^{-1}}dW_t.\]

with $X_0 = -1$. Now, we wish to find the nonequiblrium steady state (NESS) such that when the process arrives in the set $B=[0.5, \infty)$, it restarts at $-1$. We can accomplish this with an Euler-Maruyama discretization and imposing the constraint to reset the problem resulting in the modified process $\tilde{X}_t$. The following code plots the distribution. This can also be used to find the MFPT.

using Plots
using Printf
using Random
using ForwardDiff
using BasicMD

function V(x)
    return (x[1]^2 -1)^2

gradV! = (gradV, x)-> ForwardDiff.gradient!(gradV, V, x);

β = 5.0;
x₀ = [-1.0];
seed = 100;
Δt = 1e-2;
n_iters = 10^4; # number of samples

sampler = EM(gradV!, β, Δt);

# define the recycling function and the constraint
a = [-1.0];
b = 0.5;
function recycler!(state::BasicMD.EMState)
    if state.x[1] > b
        @. state.x = a
        gradV!(state.∇V, a)

recycler = Constraints(recycler!, trivial_constraint!, 1, 1);

X_vals = sample_trajectory(x₀, sampler, recycler, options = MDOptions(n_iters = n_iters));
histogram([X[1] for X in X_vals], label = "Samples", normalize = true, bins = 25)
Example block output