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Adaptive deep density approximation for Fokker-Planck equations (Q2135831)

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Language	Label	Description	Also known as
English	Adaptive deep density approximation for Fokker-Planck equations	scientific article

Statements

scholarly article

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

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Journal of Computational Physics

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publication date

9 May 2022

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full work available at URL

https://arxiv.org/abs/2103.11181

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zbMATH Keywords

density estimation

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flow-based generative models

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Fokker-Planck equations

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deep learning

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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The Fokker-Planck equation. Methods of solution and applications

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A class of asymptotic-preserving schemes for the Fokker-Planck-Landau equation

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A data-driven method for the steady state of randomly perturbed dynamics

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Finite Elements and Fast Iterative Solvers

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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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A proposal on machine learning via dynamical systems

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

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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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fPINNs: Fractional Physics-Informed Neural Networks

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DGM: a deep learning algorithm for solving partial differential equations

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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

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Numerical aspects for approximating governing equations using data

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Structure-Preserving Method for Reconstructing Unknown Hamiltonian Systems From Trajectory Data

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

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High-Order Collocation Methods for Differential Equations with Random Inputs

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A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

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The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications

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An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations

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Stochastic Collocation Methods on Unstructured Grids in High Dimensions via Interpolation

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Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation

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Multivariate Density Estimation

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From Knothe's Transport to Brenier's Map and a Continuation Method for Optimal Transport

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Coupling the reduced-order model and the generative model for an importance sampling estimator

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Stochastic Processes and Applications

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Approximation by superpositions of a sigmoidal function

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Optimization Methods for Large-Scale Machine Learning

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Identifiers

Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.JCP.2022.111080

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Sitelinks

Mathematics(1 entry)

mardi Publication:2135831

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