Pedram Hassanzadeh

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 18

Top Topics

Nonlinear Dynamical Systems

Data Assimilation

Top Venues

J. Comput. Phys.

Publications

Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander
On the importance of learning non-local dynamics for stable data-driven climate modeling: A 1D gravity wave-QBO testbed. CoRR (2024)
Karan Jakhar, Yifei Guan, Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh, Laura Zanna
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges. CoRR (2023)
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Eviatar Bach, Pedram Hassanzadeh
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. J. Comput. Phys. 477 (2023)
Ashesh Chattopadhyay, Pedram Hassanzadeh
Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution. CoRR (2023)
Rambod Mojgani, Daniel Wälchli, Yifei Guan, Petros Koumoutsakos, Pedram Hassanzadeh
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence. CoRR (2023)
Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji, Pedram Hassanzadeh
Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence. CoRR (2022)
Yifei Guan, Ashesh Chattopadhyay, Adam Subel, Pedram Hassanzadeh
via transfer learning. J. Comput. Phys. 458 (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: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. CoRR (2022)
Adam Subel, Yifei Guan, Ashesh Chattopadhyay, Pedram Hassanzadeh
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow. CoRR (2022)
Rambod Mojgani, Maciej Balajewicz, Pedram Hassanzadeh
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks. CoRR (2022)
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Eviatar Bach, Pedram Hassanzadeh
Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems. CoRR (2022)
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers. CoRR (2021)
Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh
Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation. CoRR (2021)
M. A. Khodkar, Pedram Hassanzadeh
A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings. J. Comput. Phys. 440 (2021)
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence. CI (2020)
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Pedram Hassanzadeh
Analog forecasting of extreme-causing weather patterns using deep learning. CoRR (2019)
Ashesh Chattopadhyay, Pedram Hassanzadeh, Krishna V. Palem, Devika Subramanian
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)
Ashesh Chattopadhyay, Pedram Hassanzadeh, Saba Pasha
A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns. CoRR (2018)