Generative Modeling of Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Mike Pritchard; Tom G. Beucler (CI, 2020)

A Fortran-Keras Deep Learning Bridge for Scientific Computing. - Jordan Ott; Mike Pritchard; Natalie Best; Erik Linstead; Milan Curcic; Pierre Baldi (Sci. Program., 2020)

Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs. - Harshini Mangipudi; Griffin Mooers; Mike Pritchard; Tom Beucler; Stephan Mandt (CoRR, 2021)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset. - Sungduk Yu; Mike Pritchard; Po-Lun Ma; Balwinder Singh; Sam Silva (CoRR, 2023)

Generative Residual Diffusion Modeling for Km-scale Atmospheric Downscaling. - Morteza Mardani; Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Chieh-Yu Chen; Cheng-Chin Liu; Arash Vahdat; Karthik Kashinath; Jan Kautz; Mike Pritchard (CoRR, 2023)

Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models. - Jerry Lin; Sungduk Yu; Tom Beucler; Pierre Gentine; David Walling; Mike Pritchard (CoRR, 2023)

DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations. - Jason Stock; Jaideep Pathak; Yair Cohen; Mike Pritchard; Piyush Garg; Dale R. Durran; Morteza Mardani; Noah D. Brenowitz (CoRR, 2024)

Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales. - Peter Manshausen; Yair Cohen; Jaideep Pathak; Mike Pritchard; Piyush Garg; Morteza Mardani; Karthik Kashinath; Simon Byrne; Noah D. Brenowitz (CoRR, 2024)