# Pedram Hassanzadeh

Orcid: 0000-0001-9425-8085
According to our database

Collaborative distances:

^{1}, Pedram Hassanzadeh authored at least 16 papers between 2018 and 2023.Collaborative distances:

## Timeline

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#### On csauthors.net:

## Bibliography

2023

Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems.

J. Comput. Phys., March, 2023

Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence.

CoRR, 2023

Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges.

CoRR, 2023

Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution.

CoRR, 2023

2022

Stable <i>a posteriori</i> LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher <i>Re</i> via transfer learning.

J. Comput. Phys., 2022

Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow.

CoRR, 2022

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence.

CoRR, 2022

Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks.

CoRR, 2022

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

CoRR, 2022

2021

A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings.

J. Comput. Phys., 2021

Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation.

CoRR, 2021

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers.

CoRR, 2021

2020

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence.

Proceedings of the CI 2020: 10th International Conference on Climate Informatics, 2020

2019

CoRR, 2019

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM.

CoRR, 2019

2018

A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns.

CoRR, 2018