Pages that link to "Item:Q2693426"
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The following pages link to An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426):
Displaying 6 items.
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
- The fully coupled thermo-mechanical dual-horizon peridynamic correspondence damage model for homogeneous and heterogeneous materials (Q6194203) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q6414718) (← links)
- Dual-horizon peridynamics modeling of coupled chemo-mechanical-damage for interface oxidation-induced cracking in thermal barrier coatings (Q6595895) (← links)
- f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
