Physics-Constrained Neural Surrogate for Domain Growth Prediction in Systems with Conserved Kinetics
This work proposes a physics-constrained deep neural network as a surrogate model to learn the microstructural evolution of a binary mixture, in which conservation of the order parameter is imposed directly on the network output as a hard constraint.
Vijay Yadav, Pallvi Pandey, M. Priya et al.
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