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Simple neural networks can accurately emulate complex aerosol microphysics in climate models, but only with careful attention to scaling and training convergence.
Unlock the power of interpretable AI: SINDy-KANs distills complex neural networks into sparse equations, revealing the underlying dynamics of systems.
ArGEnT lets you ditch hand-engineered geometry parameterizations in operator learning, directly encoding arbitrary shapes into a Transformer for better accuracy and generalization.
