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GINNs: graph-informed neural networks for multiscale physics - MaRDI portal

GINNs: graph-informed neural networks for multiscale physics (Q2120776)

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scientific article; zbMATH DE number 7501599

Language	Label	Description	Also known as
English	GINNs: graph-informed neural networks for multiscale physics	scientific article; zbMATH DE number 7501599

Statements

scholarly article

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GINNs: graph-informed neural networks for multiscale physics (English)

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Søren Taverniers

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Markos A. Katsoulakis

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Daniel M. Tartakovsky

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Journal of Computational Physics

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publication date

1 April 2022

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zbMATH Keywords

deep learning

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surrogate model

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Bayesian network

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probabilistic graphical model (PGM)

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uncertainty propagation

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electrical double layer capacitor (EDLC)

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describes a project that uses

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MaRDI profile type

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full work available at URL

https://doi.org/10.1016/j.jcp.2021.110192

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The role of statistics in data-centric engineering

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Path-space variational inference for non-equilibrium coarse-grained systems

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Evaluation of convergence behavior of metamodeling techniques for bridging scales in multi-scale multimaterial simulation

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An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

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Causality and Bayesian network PDEs for multiscale representations of porous media

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Neural algorithm for solving differential equations

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Deep UQ: learning deep neural network surrogate models for high dimensional 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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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Adversarial uncertainty quantification in physics-informed neural networks

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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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DGM: a deep learning algorithm for solving partial differential equations

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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

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

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Estimation of distributions via multilevel Monte Carlo with stratified sampling

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Causal inference in statistics. A primer

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

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

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Kernel density estimation via diffusion

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Dispersion controlled by permeable surfaces: surface properties and scaling

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Identifiers

Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.JCP.2021.110192

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Sitelinks

Mathematics(1 entry)

mardi Publication:2120776

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