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Found 2 papers, 1 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.
Parsimonious neural networks learn interpretable physical laws
Machine learning is playing an increasing role in the physical sciences and significant progress has been made towards embedding domain knowledge into models.
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