Neural Parametric Fokker--Planck Equation
نویسندگان
چکیده
In this paper, we develop and analyze numerical methods for high dimensional Fokker-Planck equations by leveraging generative models from deep learning. Our starting point is a formulation of the equation as system ordinary differential (ODEs) on finite-dimensional parameter space with parameters inherited such normalizing flows. We call ODEs neural parametric equations. The fact that can be viewed $L^2$-Wasserstein gradient flow Kullback-Leibler (KL) divergence allows us to derive constrained KL set probability densities generated networks. For computation, design variational semi-implicit scheme time discretization proposed ODE. Such an algorithm sampling-based, which readily handle in higher spaces. Moreover, also establish bounds asymptotic convergence analysis well error both continuous discrete versions. Several examples are provided illustrate performance algorithms analysis.
منابع مشابه
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ژورنال
عنوان ژورنال: SIAM Journal on Numerical Analysis
سال: 2022
ISSN: ['0036-1429', '1095-7170']
DOI: https://doi.org/10.1137/20m1344986