Pages that link to "Item:Q2145130"
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The following pages link to Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130):
Displaying 8 items.
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
- Discovering phase field models from image data with the pseudo-spectral physics informed neural networks (Q2667357) (← links)
- Transfer learning of recurrent neural network‐based plasticity models (Q6148497) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- Neural cellular automata for solidification microstructure modelling (Q6171252) (← links)
- Dual order-reduced Gaussian process emulators (DORGP) for quantifying high-dimensional uncertain crack growth using limited and noisy data (Q6194158) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- L-MAU: a multivariate time-series network for predicting the Cahn-Hilliard microstructure evolutions via low-dimensional approaches (Q6649117) (← links)
