Pages that link to "Item:Q2072501"
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The following pages link to A data-driven peridynamic continuum model for upscaling molecular dynamics (Q2072501):
Displaying 15 items.
- Implementing peridynamics within a molecular dynamics code (Q711072) (← links)
- Data-driven learning of nonlocal physics from high-fidelity synthetic data (Q2021231) (← links)
- Data-driven molecular modeling with the generalized Langevin equation (Q2124588) (← links)
- Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
- A machine-learning framework for peridynamic material models with physical constraints (Q2246256) (← links)
- A meshfree peridynamic model for brittle fracture in randomly heterogeneous materials (Q2674080) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- Coupling of an atomistic model and bond-based peridynamic model using an extended Arlequin framework (Q2679437) (← links)
- Peridynamics as an Upscaling of Molecular Dynamics (Q3549908) (← 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)
- A data-driven peridynamic continuum model for upscaling molecular dynamics (Q6374959) (← links)
- Peridynamic neural operators: a data-driven nonlocal constitutive model for complex material responses (Q6497150) (← links)
- A numerical procedure for fractional-time-space differential equations with the spectral fractional Laplacian (Q6612885) (← links)
- A novel framework for fatigue cracking and life prediction: perfect combination of peridynamic method and deep neural network (Q6663335) (← links)
