PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357)

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

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instance of

scholarly article

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title

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

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DOI

10.1016/j.jcp.2020.110079

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Journal of Computational Physics

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publication date

21 April 2022

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full work available at URL

https://arxiv.org/abs/2004.13145

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Mathematics Subject Classification ID

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physics-informed neural networks

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label-free

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surrogate modeling

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physics-constrained deep learning

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partial differential equations

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Navier-Stokes

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Wikidata QID

Q114163480

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describes a project that uses

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MaRDI publication profile

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OpenAlex ID

W3111914315

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cites work

DGM: a deep learning algorithm for solving partial differential equations

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

0 references

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

0 references

Machine Learning for Fluid Mechanics

0 references

Physics-informed multi-LSTM networks for metamodeling of nonlinear structures

0 references

Multilayer feedforward networks are universal approximators

0 references

Approximation by superpositions of a sigmoidal function

0 references

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks

0 references

Neural algorithm for solving differential equations

0 references

Deep learning of vortex-induced vibrations

0 references

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

0 references

Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

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Physics-informed neural networks for high-speed flows

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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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Data-driven discovery of PDEs in complex datasets

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Adversarial uncertainty quantification in physics-informed neural networks

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Solving high-dimensional partial differential equations using deep learning

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Weak adversarial networks for high-dimensional partial differential equations

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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

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MgNet: a unified framework of multigrid and convolutional neural network

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

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Prediction of aerodynamic flow fields using convolutional neural networks

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Automatic numerical generation of body-fitted curvilinear coordinate system for field containing any number of arbitrary two-dimensional bodies

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Numerical study of the turbulent flow past an airfoil with trailing edge separation

0 references

A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations

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Probabilistic Principal Component Analysis

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Mathematics(1 entry)

mardi Publication:2128357