Search Results for author: Peter Harrington

Found 12 papers, 8 papers with code

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction

1 code implementation30 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.

A Practical Probabilistic Benchmark for AI Weather Models

no code implementations27 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.

Weather Forecasting

Towards Stability of Autoregressive Neural Operators

1 code implementation18 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.

Weather Forecasting

Generative Modeling of High-resolution Global Precipitation Forecasts

no code implementations22 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.

Generative Adversarial Network Precipitation Forecasting +2

Mining for Strong Gravitational Lenses with Self-supervised Learning

1 code implementation30 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.

Representation Learning Self-Supervised Learning

Fast, high-fidelity Lyman $α$ forests with convolutional neural networks

1 code implementation23 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.

Vocal Bursts Intensity Prediction

Self-Supervised Representation Learning for Astronomical Images

1 code implementation24 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.

Astronomy Contrastive Learning +4

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