Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826)

scientific article; zbMATH DE number 7766138

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English	Adaptive deep density approximation for fractional Fokker-Planck equations	scientific article; zbMATH DE number 7766138

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instance of

scholarly article

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title

Adaptive deep density approximation for fractional Fokker-Planck equations (English)

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arXiv ID

2210.14402

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DOI

10.1007/s10915-023-02379-z

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published in

Journal of Scientific Computing

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publication date

16 November 2023

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full work available at URL

https://arxiv.org/abs/2210.14402

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Mathematics Subject Classification ID

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fractional Fokker-Planck equation

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normalizing flow

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adaptive density approximation

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Monte Carlo sampling

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Gaussian radial basis functions

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MaRDI profile type

MaRDI publication profile

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OpenAlex ID

W4388304013

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cites work

On a structure-preserving numerical method for fractional Fokker-Planck equations

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Machine Learning for Fluid Mechanics

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A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions

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Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

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Finite Element Method for the Space and Time Fractional Fokker–Planck Equation

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Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

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Fokker-Planck equations for stochastic dynamical systems with symmetric Lévy motions

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Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations

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Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models

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Solving high-dimensional partial differential equations using deep learning

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Neural Parametric Fokker--Planck Equation

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A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems

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fPINNs: Fractional Physics-Informed Neural Networks

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Q4998956

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DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations

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Lévy flights and related topics in physics. Proceedings of the international workshop, held at Nice, France, 27-30 June, 1994

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DGM: a deep learning algorithm for solving partial differential equations

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Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks

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Adaptive deep density approximation for Fokker-Planck equations

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DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations

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Variable kernel density estimation

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The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

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Path integral solutions of the governing equation of SDEs excited by Lévy white noise

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Adversarial uncertainty quantification in physics-informed neural networks

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Stochastic dynamics driven by combined Lévy–Gaussian noise: fractional Fokker–Planck–Kolmogorov equation and solution

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Weak adversarial networks for high-dimensional partial differential equations

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A time-space spectral method for the time-space fractional Fokker-Planck equation and its inverse problem

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Sitelinks

Mathematics(1 entry)

mardi Publication:6087826