Pages that link to "Item:Q2142205"
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The following pages link to Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205):
Displaying 10 items.
- GraFEA: a graph-based finite element approach for the study of damage and fracture in brittle materials (Q1678667) (← links)
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
- Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation (Q2678544) (← links)
- Rutting prediction and analysis of influence factors based on multivariate transfer entropy and graph neural networks (Q6053228) (← links)
- Deep learning phase‐field model for brittle fractures (Q6071412) (← links)
- Dual order-reduced Gaussian process emulators (DORGP) for quantifying high-dimensional uncertain crack growth using limited and noisy data (Q6194158) (← links)
- Multiscale graph neural networks with adaptive mesh refinement for accelerating mesh-based simulations (Q6588310) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Model-free chemomechanical interfaces: history-dependent damage under transient mass diffusion (Q6609801) (← links)
- The novel graph transformer-based surrogate model for learning physical systems (Q6643571) (← links)
