Pedram Hassanzadeh
Orcid: 0000-0001-9425-8085
According to our database1,
Pedram Hassanzadeh
authored at least 16 papers
between 2018 and 2023.
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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