Alexander Wikner

University of Chicago — United States

Earth and Planetary Sciences · Physical Sciences

7h-index322citations17works0.32yr avg
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Research Topics

Meteorological Phenomena and Simulations(11), Climate variability and models(7), Neural Networks and Reservoir Computing(7), Model Reduction and Neural Networks(4), Hydrological Forecasting Using AI(3)

Publications17 total

AI-boosted rare event sampling to characterize extreme weather
ArXiv.org·2025· 1 citedOpen Access
Reframing Generative Models for Physical Systems using Stochastic Interpolants
arXiv (Cornell University)·2025Open Access
Beyond the Unseen: Assessing AI Climate Emulators’ Capacity to Simulate Very Rare Events
2025Open Access
Coupling AI Emulators and Rare Event Algorithms to Sample Extreme Heatwaves
2025Open Access
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing
Neural Networks·2023· 19 citedOpen Access
Attention-Based Ensemble Pooling for Time Series Forecasting
arXiv (Cornell University)·2023Open Access
Data-informed reservoir computing for efficient time-series prediction
Chaos An Interdisciplinary Journal of Nonlinear Science·2023· 30 citedOpen Access
A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component
Geophysical Research Letters·2023· 24 citedOpen Access
A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component
2022· 2 citedOpen Access
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
arXiv (Cornell University)·2022· 2 citedOpen Access
A Hybrid Approach to Atmospheric Modeling That Combines Machine Learning With a Physics‐Based Numerical Model
Journal of Advances in Modeling Earth Systems·2022· 58 citedOpen Access
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
2021· 3 citedOpen Access
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
2021· 9 citedOpen Access
Α Hybrid Approach to Atmospheric Modeling that Combines Machine Learning with a Physics-Based Numerical Model
2021· 1 citedOpen Access
A Machine Learning‐Based Global Atmospheric Forecast Model
Geophysical Research Letters·2020· 156 citedOpen Access
A Machine-Learning-Based Global Atmospheric Forecast Model
2020· 10 citedOpen Access
A Machine-Learning-Based Global Atmospheric Forecast Model
2020· 7 citedOpen Access

Frequent Co-authors

Brian R. Hunt(11), Edward Ott(11), Troy Arcomano(10), Istvan Szunyogh(9), Jaideep Pathak(7), Amaury Lancelin(4), Pedram Hassanzadeh(4), Dhruvit Patel(3), Freddy Bouchet(3), Joseph E. Harvey(2), Michelle Girvan(2), Laurent Dubus(2), Dorian S. Abbot(2), Jonathan Weare(2), Felix Köster(1), Lina Jaurigue(1), Kathy Lüdge(1), Andrew Pomerance(1), Thomas M. Antonsen(1), Clément Le Priol(1)