A universal shallow water equations solver integrates neural networks via differentiable programming to enable inverse modeling of flow resistance and physics discovery in river channels.
Should we apply bias correction to global and regional climate model data?
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dCLIMBA learns parametric bias corrections for GCM precipitation using differentiability to match observations, improving extreme distributions and showing partial generalization.
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Scientific Machine Learning of Flow Resistance Using Universal Shallow Water Equations with Differentiable Programming
A universal shallow water equations solver integrates neural networks via differentiable programming to enable inverse modeling of flow resistance and physics discovery in river channels.
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A Differentiable Framework for Global Circulation Model Precipitation Bias Correction
dCLIMBA learns parametric bias corrections for GCM precipitation using differentiability to match observations, improving extreme distributions and showing partial generalization.