Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (Q6121800)

scientific article; zbMATH DE number 7824905

Language	Label	Description	Also known as
English	Adversarial deep energy method for solving saddle point problems involving dielectric elastomers	scientific article; zbMATH DE number 7824905

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instance of

scholarly article

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title

Adversarial deep energy method for solving saddle point problems involving dielectric elastomers (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

26 March 2024

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zbMATH Keywords

physics-informed neural networks

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deep energy method

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saddle point problem

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adversarial networks

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dielectric elastomers

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.cma.2024.116825

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cites work

An overset grid method for integration of fully 3D fluid dynamics and geophysics fluid dynamics models to simulate multiphysics coastal ocean flows

0 references

Load and motion transfer algorithms for fluid/structure interaction problems with non-matching discrete interfaces: Momentum and energy conservation, optimal discretization and application to aeroelasticity

0 references

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

0 references

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

0 references

Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

0 references

Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems

0 references

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

0 references

A deep energy method for finite deformation hyperelasticity

0 references

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

0 references

Parametric deep energy approach for elasticity accounting for strain gradient effects

0 references

A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches

0 references

The mixed deep energy method for resolving concentration features in finite strain hyperelasticity

0 references

A deep learning energy method for hyperelasticity and viscoelasticity

0 references

Physics-informed neural networks for shell structures

0 references

On the perturbed Lagrangian formulation for nearly incompressible and incompressible hyperelasticity

0 references

Variational modeling of hydromechanical fracture in saturated porous media: a micromechanics-based phase-field approach

0 references

A computational framework for large strain nearly and truly incompressible electromechanics based on convex multi-variable strain energies

0 references

A convex multi-variable based computational framework for multilayered electro-active polymers

0 references

A polyconvex transversely-isotropic invariant-based formulation for electro-mechanics: stability, minimisers and computational implementation

0 references

A multiscale FE-FFT framework for electro-active materials at finite strains

0 references

A new framework for large strain electromechanics based on convex multi-variable strain energies: variational formulation and material characterisation

0 references

Weak adversarial networks for high-dimensional partial differential equations

0 references

A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks

0 references

On the spatial and material motion problems in nonlinear electro-elastostatics with consideration of free space

0 references

A robust and computationally efficient finite element framework for coupled electromechanics

0 references

A numerical framework for modeling anisotropic dielectric elastomers

0 references

A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials

0 references

A variational approach to nonlinear electro-magneto-elasticity: Convexity conditions and existence theorems

0 references

Topological properties of the set of functions generated by neural networks of fixed size

0 references

A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

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On quadrature rules for solving partial differential equations using neural networks

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Identifiers

DOI

10.1016/j.cma.2024.116825

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6121800