Accurate segmentation of brain tumors from multi-modal MRI scans is critical for diagnosis, treatment planning, and disease monitoring.
To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions.
The present study explores the potential of super-resolution machine learning (ML) models for precipitation downscaling from 100 to 12.5 km...
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In silico, medicine models are frequently used to represent a phenomenon across multiples space–time scales. Most of these multiscale models...
This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow...
We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions.
In this study, we evaluate the ability of pore network models (PNMs) to characterize multiphase flow heterogeneity at the millimeter scale.
Tectonic deformation induces multiscale modifications in the pore–fracture morphology of coal, fundamentally altering its seepage behavior.
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