Pages that link to "Item:Q2670357"
From MaRDI portal
The following pages link to Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites (Q2670357):
Displaying 15 items.
- CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (Q1982319) (← links)
- Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning (Q1986877) (← links)
- Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
- Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
- Simulation of Maxwell equation based on an ADI approach and integrated radial basis function-generalized moving least squares (IRBF-GMLS) method with reduced order algorithm based on proper orthogonal decomposition (Q2085936) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
- FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
- (Q5852424) (← links)
- A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
- Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics (Q6084492) (← links)
- A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content (Q6096512) (← links)
- Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading (Q6164292) (← links)
- A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
- Exploring the roles of numerical simulations and machine learning in multiscale paving materials analysis: applications, challenges, best practices (Q6663259) (← links)
