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Found 3 papers, 2 papers with code
Rethinking materials simulations: Blending direct numerical simulations with neural operators
This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism that enables accurate extrapolation and efficient time-to-solution predictions of the dynamics.
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space.
Inferring topological transitions in pattern-forming processes with self-supervised learning
The identification and classification of transitions in topological and microstructural regimes in pattern-forming processes are critical for understanding and fabricating microstructurally precise novel materials in many application domains.
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