Search Results for author: Michael Pritchard

Found 5 papers, 3 papers with code

Climate-Invariant Machine Learning

1 code implementation14 Dec 2021 Tom Beucler, Michael Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Pierre Gentine

Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution climate models when trained on high-resolution simulation data; however, they often make large generalization errors when evaluated in conditions they were not trained on.

BIG-bench Machine Learning

Generative Modeling for Atmospheric Convection

no code implementations3 Jul 2020 Griffin Mooers, Jens Tuyls, Stephan Mandt, Michael Pritchard, Tom Beucler

While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources.

Dimensionality Reduction Representation Learning

Towards Physically-consistent, Data-driven Models of Convection

4 code implementations20 Feb 2020 Tom Beucler, Michael Pritchard, Pierre Gentine, Stephan Rasp

Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.

Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems

4 code implementations3 Sep 2019 Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, Pierre Gentine

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.

Computational Physics Atmospheric and Oceanic Physics

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

no code implementations15 Jun 2019 Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.

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