P
Pedram Hassanzadeh
Researcher at Rice University
Publications - 94
Citations - 2299
Pedram Hassanzadeh is an academic researcher from Rice University. The author has contributed to research in topics: Vortex & Computer science. The author has an hindex of 21, co-authored 77 publications receiving 1434 citations. Previous affiliations of Pedram Hassanzadeh include Colorado State University & University of California, Berkeley.
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Journal ArticleDOI
Zombie vortex instability. i. a purely hydrodynamic instability to resurrect the dead zones of protoplanetary disks
Philip Marcus,Suyang Pei,Chung-Hsiang Jiang,Joseph Barranco,Pedram Hassanzadeh,Daniel Lecoanet +5 more
TL;DR: In this paper, the authors present simulations with a pseudo-spectral anelastic code and with the compressible code Athena, showing that stably stratified flows in a shearing, rotating box are violently unstable and produce space-filling, sustained turbulence dominated by large vortices with Rossby numbers of order ~0.2-0.3.
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Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
TL;DR: It is shown that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales.
Journal Article
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak,Shashank Subramanian,Peter B. Harrington,S. Senthil Raja,Ashesh Chattopadhyay,Morteza Mardani,Thorsten Kurth,David Hall,Zongyi Li,Kamyar Azizzadenesheli,Pedram Hassanzadeh,Karthik Kashinath,Animashree Anandkumar +12 more
TL;DR: In this article , the Fourier Forecasting Neural Network (FCN) is used to forecast high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
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Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM
TL;DR: In this article, the response of atmospheric blocks and the wave amplitude of midlatitude jets to changes in the mid-latitude to pole, near-surface temperature difference (ΔT) was studied using an idealized dry general circulation model (GCM) with Held-Suarez forcing.
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Physics-informed machine learning: case studies for weather and climate modelling.
Karthik Kashinath,Mustafa Mustafa,Adrian Albert,Jin-Long Wu,Jin-Long Wu,Chiyu "Max" Jiang,Chiyu "Max" Jiang,Soheil Esmaeilzadeh,Kamyar Azizzadenesheli,Rui Wang,Rui Wang,Ashesh Chattopadhyay,Ashesh Chattopadhyay,A. Singh,Ashray Manepalli,Dragos B. Chirila,Rose Yu,Robin Walters,Brian White,Heng Xiao,Hamdi A. Tchelepi,Philip Marcus,Animashree Anandkumar,Animashree Anandkumar,Pedram Hassanzadeh,Prabhat +25 more
TL;DR: In this paper, the authors survey systematic approaches to incorporating physics and domain knowledge into ML models and distill these approaches into broad categories, and show how these approaches have been used successfully for emulating, downscaling, and forecasting weather and climate processes.