Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11223
For citation please use:
Main Title: Interacting particle solutions of Fokker–Planck equations through gradient–log–density estimation
Author(s): Maoutsa, Dimitra
Reich, Sebastian
Opper, Manfred
Type: Article
Language Code: en
Abstract: Fokker–Planck equations are extensively employed in various scientific fields as they characterise the behaviour of stochastic systems at the level of probability density functions. Although broadly used, they allow for analytical treatment only in limited settings, and often it is inevitable to resort to numerical solutions. Here, we develop a computational approach for simulating the time evolution of Fokker–Planck solutions in terms of a mean field limit of an interacting particle system. The interactions between particles are determined by the gradient of the logarithm of the particle density, approximated here by a novel statistical estimator. The performance of our method shows promising results, with more accurate and less fluctuating statistics compared to direct stochastic simulations of comparable particle number. Taken together, our framework allows for effortless and reliable particle-based simulations of Fokker–Planck equations in low and moderate dimensions. The proposed gradient–log–density estimator is also of independent interest, for example, in the context of optimal control.
URI: https://depositonce.tu-berlin.de/handle/11303/12382
http://dx.doi.org/10.14279/depositonce-11223
Issue Date: 22-Jul-2020
Date Available: 11-Jan-2021
DDC Class: 510 Mathematik
Subject(s): stochastic systems
Fokker-Planck equation
interacting particles
multiplicative noise
gradient flow
stochastic differential equations
Sponsor/Funder: TU Berlin, Open-Access-Mittel – 2020
DFG, 318763901, SFB 1294: Datenassimilation: Die nahtlose Verschmelzung von Daten und Modellen
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Entropy
Publisher: MDPI
Publisher Place: Basel
Volume: 22
Issue: 8
Article Number: 802
Publisher DOI: 10.3390/e22080802
EISSN: 1099-4300
Appears in Collections:FG Methoden der Künstlichen Intelligenz » Publications

Files in This Item:

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons