A graph-based automated model discovery framework identifies new concise soil hydraulic functions from data that outperform the Mualem-van Genuchten model across 249 soil samples.
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Transformers trained on simulation data can predict atomistic transitions in nano-clusters while allowing generation of varied microstates and checks for physical validity.
SMT reformulates imaging through scattering media as a Zernike-regularized coarse-to-fine optimization on spectrally-resolved scattering matrices, achieving depth-over-resolution ratios above 900 in ex vivo mouse brain and volumetric imaging beyond three transport mean free paths in colloids.
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Graph-based automated discovery of concise soil hydraulic functions from data: beyond the Mualem - van Genuchten model
A graph-based automated model discovery framework identifies new concise soil hydraulic functions from data that outperform the Mualem-van Genuchten model across 249 soil samples.
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Predicting Atomistic Transitions with Transformers
Transformers trained on simulation data can predict atomistic transitions in nano-clusters while allowing generation of varied microstates and checks for physical validity.
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Adaptive Optical Multi-Spectral Matrix Approach for Label-free High-resolution Imaging through Complex Scattering Media
SMT reformulates imaging through scattering media as a Zernike-regularized coarse-to-fine optimization on spectrally-resolved scattering matrices, achieving depth-over-resolution ratios above 900 in ex vivo mouse brain and volumetric imaging beyond three transport mean free paths in colloids.