A data-driven ANN method estimates low-lying Fokker-Planck and Koopman eigenfunctions from Monte Carlo density relaxations, and is used to define a stochastic asymptotic phase for the noisy Lorenz system.
A data-driven method for the steady state of randomly perturbed dynamics
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abstract
We demonstrate a data-driven method to solve for the invariant probability density function of a randomly perturbed dynamical system. The key idea is to replace the boundary condition of numerical schemes by a least squares problem corresponding to a reference solution, which is generated by Monte Carlo simulation. With this method we can solve for the invariant probability density function in any local area with high accuracy, regardless of whether the attractor is covered by the numerical domain.
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Artificial neural network solver for Fokker-Planck and Koopman eigenfunctions
A data-driven ANN method estimates low-lying Fokker-Planck and Koopman eigenfunctions from Monte Carlo density relaxations, and is used to define a stochastic asymptotic phase for the noisy Lorenz system.