Adversarial deep energy method for solving saddle point problems involving dielectric elastomers
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Publication:6121800
DOI10.1016/j.cma.2024.116825OpenAlexW4391629742WikidataQ128221576 ScholiaQ128221576MaRDI QIDQ6121800
Chien Truong-Quoc, Seung Woo Lee, Do-Nyun Kim, Youngmin Ro
Publication date: 26 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2024.116825
saddle point problemdielectric elastomersadversarial networksphysics-informed neural networksdeep energy method
Cites Work
- A three-dimensional constitutive model for the large stretch behavior of rubber elastic materials
- An overset grid method for integration of fully 3D fluid dynamics and geophysics fluid dynamics models to simulate multiphysics coastal ocean flows
- Weak adversarial networks for high-dimensional partial differential equations
- On the perturbed Lagrangian formulation for nearly incompressible and incompressible hyperelasticity
- 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
- A numerical framework for modeling anisotropic dielectric elastomers
- A multiscale FE-FFT framework for electro-active materials at finite strains
- A robust and computationally efficient finite element framework for coupled electromechanics
- \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
- A convex multi-variable based computational framework for multilayered electro-active polymers
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Topological properties of the set of functions generated by neural networks of fixed size
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
- Physics-informed neural networks for shell structures
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
- On quadrature rules for solving partial differential equations using neural networks
- Variational modeling of hydromechanical fracture in saturated porous media: a micromechanics-based phase-field approach
- A physics-guided neural network framework for elastic plates: comparison of governing equations-based and energy-based approaches
- Parametric deep energy approach for elasticity accounting for strain gradient effects
- A deep energy method for finite deformation hyperelasticity
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- A computational framework for large strain nearly and truly incompressible electromechanics based on convex multi-variable strain energies
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A new framework for large strain electromechanics based on convex multi-variable strain energies: variational formulation and material characterisation
- A deep learning energy method for hyperelasticity and viscoelasticity
- A polyconvex transversely-isotropic invariant-based formulation for electro-mechanics: stability, minimisers and computational implementation
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios
- A deep double Ritz method (\(\mathrm{D^2RM}\)) for solving partial differential equations using neural networks
- On the spatial and material motion problems in nonlinear electro-elastostatics with consideration of free space
- A variational approach to nonlinear electro-magneto-elasticity: Convexity conditions and existence theorems
