1 code implementation • 30 Apr 2024 • Jared D. Willard, Peter Harrington, Shashank Subramanian, Ankur Mahesh, Travis A. O'Brien, William D. Collins
The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP.
no code implementations • 27 Jan 2024 • Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Ankur Mahesh, Boris Bonev, Thorsten Kurth, Dale R. Durran, Peter Harrington, Michael S. Pritchard
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
1 code implementation • 18 Jun 2023 • Michael McCabe, Peter Harrington, Shashank Subramanian, Jed Brown
Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences.
no code implementations • 22 Oct 2022 • James Duncan, Shashank Subramanian, Peter Harrington
Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change.
no code implementations • 8 Aug 2022 • Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
4 code implementations • 22 Feb 2022 • Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
2 code implementations • 25 Oct 2021 • George Stein, Peter Harrington, Jacqueline Blaum, Tomislav Medan, Zarija Lukic
We present the use of self-supervised learning to explore and exploit large unlabeled datasets.
1 code implementation • 30 Sep 2021 • George Stein, Jacqueline Blaum, Peter Harrington, Tomislav Medan, Zarija Lukic
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9.
1 code implementation • 23 Jun 2021 • Peter Harrington, Mustafa Mustafa, Max Dornfest, Benjamin Horowitz, Zarija Lukić
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources.
no code implementations • 12 Jan 2021 • Md Abul Hayat, Peter Harrington, George Stein, Zarija Lukić, Mustafa Mustafa
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
1 code implementation • 24 Dec 2020 • Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić, Mustafa Mustafa
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
1 code implementation • 25 Jul 2020 • Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks.