2 citations · 3 across the 5 of their papers we have counts for
5 papers
A Next-Generation Snow Albedo Parameterization for Climate Modeling using Constrained Machine Learning
Andrew Charbonneau, Katherine Deck, Tapio Schneider
We demonstrate a data-driven parameterization for snow albedo using a constrained neural differential equation that directly predicts a range of snow albedo tendencies from standar…
Surface to Seafloor: A Generative AI Framework for Decoding the Ocean Interior State
Andre N. Souza, Simone Silvestri, Katherine Deck +3
Understanding subsurface ocean dynamics is essential for quantifying oceanic heat and mass transport, but direct observations at depth remain sparse due to logistical and technolog…
A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
Andrew Charbonneau, Katherine Deck, Tapio Schneider
This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydro…
Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar +1
Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for…
Response Theory via Generative Score Modeling
Ludovico Theo Giorgini, Katherine Deck, Tobias Bischoff +1
We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the Generalized Fluctuation-D…