Pore-scale disorder accelerates fluid stretching in porous media, producing quadratic time growth and faster mixing than the linear growth seen in ordered structures.
Gallego-Posada, J
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Fixed penalty methods in deep learning do not reliably solve problems with hard non-negotiable constraints, so the constrained formulation should be the starting point instead.
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How pore-scale disorder controls fluid stretching in porous media
Pore-scale disorder accelerates fluid stretching in porous media, producing quadratic time growth and faster mixing than the linear growth seen in ordered structures.
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Position: Adopt Constraints Over Fixed Penalties in Deep Learning
Fixed penalty methods in deep learning do not reliably solve problems with hard non-negotiable constraints, so the constrained formulation should be the starting point instead.