Pages that link to "Item:Q2246256"
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The following pages link to A machine-learning framework for peridynamic material models with physical constraints (Q2246256):
Displaying 14 items.
- A data-driven peridynamic continuum model for upscaling molecular dynamics (Q2072501) (← links)
- On the prescription of boundary conditions for nonlocal Poisson's and peridynamics models (Q2080554) (← links)
- Anisotropic peridynamics for homogenized microstructured materials (Q2138722) (← links)
- An optimization-based approach to parameter learning for fractional type nonlocal models (Q2147289) (← links)
- Efficient optimization-based quadrature for variational discretization of nonlocal problems (Q2156793) (← links)
- A discussion on nonlocality: from fractional derivative model to peridynamic model (Q2160920) (← links)
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (Q2226818) (← links)
- Machine learning of nonlocal micro-structural defect evolutions in crystalline materials (Q2679512) (← links)
- New Insights on Convergence Properties of Peridynamic Models for Transient Diffusion and Elastodynamics (Q5878920) (← links)
- A data‐driven bond‐based peridynamic model derived from group method of data handling neural network with genetic algorithm (Q6092283) (← links)
- An Asymptotically Compatible Coupling Formulation for Nonlocal Interface Problems with Jumps (Q6108168) (← links)
- Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics (Q6173029) (← links)
- A machine-learning framework for peridynamic material models with physical constraints (Q6357555) (← links)
- A novel framework for fatigue cracking and life prediction: perfect combination of peridynamic method and deep neural network (Q6663335) (← links)
