17 citations · 25 across the 12 of their papers we have counts for
13 papers
Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems
Qi Liu, Laure Zanna, Joan Bruna
Recent advances in autoregressive neural surrogate models have enabled orders-of-magnitude speedups in simulating dynamical systems. However, autoregressive models are generally pr…
Accelerating scientific discovery with the common task framework
J. Nathan Kutz, Peter Battaglia, Michael Brenner +12
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical,…
A Framework for Hybrid Physics-AI Coupled Ocean Models
Laure Zanna, William Gregory, Pavel Perezhogin +23
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Par…
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
James P. C. Duncan, Elynn Wu, Surya Dheeshjith +15
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land sur…
Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
William Gregory, Mitchell Bushuk, Yong-Fei Zhang +4
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fu…
Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
Pavel Perezhogin, Alistair Adcroft, Laure Zanna
Data-driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly in generalization tasks and may require retuni…
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Moein Darman, Pedram Hassanzadeh, Laure Zanna +1
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and climate prediction and turbulence modeling. TL…
The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s
Jia-Rui Shi, Laure Zanna, Alistair Adcroft
We investigate the temporal evolution of ocean heat uptake efficiency (OHUE) using observations and large ensemble model simulations. OHUE, defined as the ratio of the rate in ocea…
CAMulator: Fast Emulation of the Community Atmosphere Model
William E. Chapman, John S. Schreck, Yingkai Sha +5
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state give…
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos
Chris Pedersen, Laure Zanna, Joan Bruna
Autoregressive surrogate models (or \textit{emulators}) of spatiotemporal systems provide an avenue for fast, approximate predictions, with broad applications across science and en…
Samudra: An AI Global Ocean Emulator for Climate
Surya Dheeshjith, Adam Subel, Alistair Adcroft +4
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulat…
An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Cem Gultekin, Adam Subel, Cheng Zhang +5
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the…
A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Qidong Yang, Weicheng Zhu, Joseph Keslin +3
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often de…