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DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method - MaRDI portal

DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762)

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English	DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method	scientific article

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scholarly article

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DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (English)

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Journal of Computational Physics

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publication date

13 June 2022

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full work available at URL

https://arxiv.org/abs/2111.01356

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zbMATH Keywords

physically parameterized invariant measures

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interacting particle method

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deep learning

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Wasserstein distance

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front speeds in chaotic flows

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Numerical solution of inverse problems by weak adversarial networks

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Connections between deep learning and partial differential equations

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Least-squares ReLU neural network (LSNN) method for linear advection-reaction equation

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Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs

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Physics-informed machine learning for reduced-order modeling of nonlinear problems

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Stretch, Twist, Fold: The Fast Dynamo

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Approximation by superpositions of a sigmoidal function

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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations

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

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Functional Integration and Partial Differential Equations. (AM-109)

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Solving high-dimensional partial differential equations using deep learning

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Relu Deep Neural Networks and Linear Finite Elements

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MgNet: a unified framework of multigrid and convolutional neural network

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Multilayer feedforward networks are universal approximators

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A Semi-Lagrangian Computation of Front Speeds of G-equation in ABC and Kolmogorov Flows with Estimation via Ballistic Orbits

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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Solving parametric PDE problems with artificial neural networks

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Deep learning in fluid dynamics

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Neural algorithm for solving differential equations

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Computations of optimal transport distance with Fisher information regularization

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PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network

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Global convergence and geometric characterization of slow to fast weight evolution in neural network training for classifying linearly non-separable data

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Deep Network Approximation for Smooth Functions

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A Convergent Interacting Particle Method and Computation of KPP Front Speeds in Chaotic Flows

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Computing Residual Diffusivity by Adaptive Basis Learning via Spectral Method

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The numerical solution of linear ordinary differential equations by feedforward neural networks

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New Error Bounds for Deep ReLU Networks Using Sparse Grids

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Existence of KPP fronts in spatially-temporally periodic advection and variational principle for propagation speeds

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Data driven governing equations approximation using deep neural networks

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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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Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

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Combinatorial optimization. Polyhedra and efficiency (3 volumes)

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Finite Element Computation of KPP Front Speeds in 3D Cellular and ABC Flows

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Finite element computation of KPP front speeds in cellular and cat's eye flows

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Deep Network With Approximation Error Being Reciprocal of Width to Power of Square Root of Depth

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A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices

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DGM: a deep learning algorithm for solving partial differential equations

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Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

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Deep multiscale model learning

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A mesh-free method for interface problems using the deep learning approach

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Data-driven deep learning of partial differential equations in modal space

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An Introduction to Fronts in Random Media

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

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Error bounds for approximations with deep ReLU networks

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Weak adversarial networks for high-dimensional 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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A computational study of residual KPP front speeds in time-periodic cellular flows in the small diffusion limit

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Identifiers

10.1016/j.jcp.2022.111309

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

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