Moral Machines? - Michael S. Pritchard (Sci. Eng. Ethics, 2012)

Engineering Ethics: Looking Back, Looking Forward. - Richard A. Burgess; Michael Davis 002; Marilyn A. Dyrud; Joseph R. Herkert; Rachelle D. Hollander; Lisa H. Newton; Michael S. Pritchard; P. Aarne Vesilind (Sci. Eng. Ethics, 2013)

Remembering Vivian Weil. - Rachelle D. Hollander; Michael Davis 002; Deni Elliott; Michael S. Pritchard (Sci. Eng. Ethics, 2017)

Deep learning to represent sub-grid processes in climate models. - Stephan Rasp 001; Michael S. Pritchard; Pierre Gentine (CoRR, 2018)

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. - Tom Beucler; Stephan Rasp 001; Michael S. Pritchard; Pierre Gentine (CoRR, 2019)

Towards Physically-Consistent, Data-Driven Models of Convection. - Tom Beucler; Michael S. Pritchard; Pierre Gentine; Stephan Rasp 001 (IGARSS, 2020)

Generative Modeling for Atmospheric Convection. - Griffin Mooers; Jens Tuyls; Stephan Mandt; Michael S. Pritchard; Tom Beucler (CoRR, 2020)

Climate-Invariant Machine Learning. - Tom Beucler; Michael S. Pritchard; Janni Yuval; Ankitesh Gupta; Liran Peng; Stephan Rasp 001; Fiaz Ahmed; Paul A. O'Gorman; J. David Neelin; Nicholas J. Lutsko; Pierre Gentine (CoRR, 2021)

Multi-fidelity climate model parameterization for better generalization and extrapolation. - Mohamed Aziz Bhouri; Liran Peng; Michael S. Pritchard; Pierre Gentine (CoRR, 2023)

ACE: A fast, skillful learned global atmospheric model for climate prediction. - Oliver Watt-Meyer; Gideon Dresdner; Jeremy McGibbon; Spencer K. Clark; Brian Henn; James Duncan 002; Noah D. Brenowitz; Karthik Kashinath; Michael S. Pritchard; Boris Bonev; Matthew E. Peters; Christopher S. Bretherton (CoRR, 2023)

Understanding and Visualizing Droplet Distributions in Simulations of Shallow Clouds. - Justus C. Will; Andrea M. Jenney; Kara D. Lamb; Michael S. Pritchard; Colleen Kaul; Po-Lun Ma; Kyle Pressel; Jacob Shpund; Marcus van Lier-Walqui; Stephan Mandt (CoRR, 2023)

A Practical Probabilistic Benchmark for AI Weather Models. - Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Ankur Mahesh; Boris Bonev; Thorsten Kurth; Dale R. Durran; Peter Harrington; Michael S. Pritchard (CoRR, 2024)

Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. - Chenggong Wang; Michael S. Pritchard; Noah D. Brenowitz; Yair Cohen; Boris Bonev; Thorsten Kurth; Dale R. Durran; Jaideep Pathak (CoRR, 2024)

Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; David Pruitt; et al. (CoRR, 2024)

Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Joshua Elms; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; et al. (CoRR, 2024)

Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling. - Jaideep Pathak; Yair Cohen; Piyush Garg; Peter Harrington; Noah D. Brenowitz; Dale R. Durran; Morteza Mardani; Arash Vahdat; Shaoming Xu; Karthik Kashinath; Michael S. Pritchard (CoRR, 2024)

Stochastic Flow Matching for Resolving Small-Scale Physics. - Stathi Fotiadis; Noah D. Brenowitz; Tomas Geffner; Yair Cohen; Michael S. Pritchard; Arash Vahdat; Morteza Mardani (CoRR, 2024)

Heavy-Tailed Diffusion Models. - Kushagra Pandey; Jaideep Pathak; Yilun Xu; Stephan Mandt; Michael S. Pritchard; Arash Vahdat; Morteza Mardani (ICLR, 2025)

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale. - Boris Bonev; Thorsten Kurth; Ankur Mahesh; Mauro Bisson; Jean Kossaifi; Karthik Kashinath; Anima Anandkumar; William D. Collins; Michael S. Pritchard; Alexander Keller (CoRR, 2025)