3 citations · 9 across the 9 of their papers we have counts for
11 papers
Bolzano: From Expert-Guided Proof Search to Automated Open-Problem Solving
Adrián Zámečník, Matěj Kripner, Martin Koutecký +5
Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verificat…
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional tempe…
Improving global precipitation forecasts with an AI weather model trained on satellite observations
Julian F. Schmitt, Bertrand Delorme, Robert C. King +4
Precipitation forecasts shape decision-making across the global economy, particularly in sectors such as agriculture. However, unlike variables such as temperature, precipitation i…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
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…
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 cas…
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…
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…
Dynamical-generative downscaling of climate model ensembles
Ignacio Lopez-Gomez, Zhong Yi Wan, Leonardo Zepeda-Núñez +3
Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state…
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…
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…