8 papers
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +306
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
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…
Regional climate risk assessment from climate models using probabilistic machine learning
Zhong Yi Wan, Ignacio Lopez-Gomez, Robert Carver +4
Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce…
The Ensemble Kalman Inversion Race
Rebecca Gjini, Matthias Morzfeld, Oliver R. A. Dunbar +1
Ensemble Kalman methods were initially developed to solve nonlinear data assimilation problems in oceanography, but are now popular in applications far beyond their original use ca…
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…
Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion
Yifei Guan, Pedram Hassanzadeh, Tapio Schneider +4
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and…